Podcasts about domino data lab

How are enterprises scaling AI and ML today? At Gartner Data & Analytics Summit, I had a great conversation with Thomas Been, CMO of Domino Data Lab on The Ravit Show, about the latest trends in enterprise AI and ML.We explored how Domino Data is helping organizations operationalize AI, real-world use cases from enterprises, and what's coming next in the next 3-6 months.With AI evolving so rapidly, it's great to see how companies are moving from experimentation to real impact. Lots of insights from this one!#GartnerDA #GenAI #GartnerOrlando2025 #theravitshow

This episode of Building the Base features an interview with Thomas Robinson, Chief Operating Officer of Domino Data Lab, who has an interesting background in finance and a strong passion for contributing to national security. Hosted by Lauren Bedula and Hondo Geurts, the conversation dives into Thomas's transition from a hedge fund to defense tech, and how his background has informed his leadership in AI and data science. Thomas credits his recent swell of patriotism to a desire to build the best possible future for his 17 month old twins. Five key takeaways from this episode: Domino Data Lab's success began in the commercial sector, and has since expanded to defense. In areas like mine detection using unmanned underwater vehicles, their technology has helped reduce model deployment times from 6 months to 2 weeks.Thomas challenges the myth that DoD is slow, saying that some DoD projects can be faster than commercial ones, especially when working with the Defense Innovation Unit (DIU), citing the successful Project Ammo for the Navy. “AI primes” bring not just systems integration skills, but also deep knowledge of data science and AI. While policy is important, the real concern for defense is trust in AI models. Thomas advocates for public-private partnerships to develop sensible regulations based on the impact of AI systems.For entrepreneurs and startups looking to break into defense tech, Thomas advises doing thorough research and preparing for the “valley of death.” Founders should plan for long-term cycles (3+ years), understand the realities of defense procurement, and be prepared to educate investors and boards about the unique challenges.

I had the incredible opportunity to host Thomas Been, CMO of Domino Data Lab, on The Ravit Show at NVIDIA's GTC event. It was a session filled with insightful discussions, revelations, and a deep dive into the ever-evolving world of Enterprise AI. Here are a few key takeaways --

On this episode of Marketing Art and Science, I am joined by Domino Data Lab CMO Thomas Been for a conversation on how Domino is enabling its customers to unleash the power of AI, how marketing is leveraging generative AI to facilitate the customer journey, and how its martech stack is influencing targeting, amplification, and engagement. Our discussion covers: How Domino Data Lab is enabling organizations to unleash the power of AI to transform leveraging marketing.  About Domino's MarTech stack and AI integration: The CMO's recommendations on AI (e.g., build the basics, learn your business, innovate); an overview of Domino's Martech stack Marketing's influence on the customer journey from prospect to advocate, emphasizing use case discovery and valuable content creation Fail to Fab: An example of turning a marketing failure into a success using technology and generative AI  

Thrilled to share highlights from The Ravit Show at Gartner! Had the opportunity to chat with my friend, Thomas Been, CMO of Domino Data Lab, about Data Science, AI initiatives and much more! Key Discussions: - Enterprise AI - Value and Production - Responsible AI/GenAI Thomas's insights were not just enlightening but actionable, offering a roadmap for leveraging AI effectively and ethically.

There's one marketing tactic that can make people laugh, it can make people gasp. But either way it'll make them remember your content for years. It's shock factor. And people aren't using it enough in B2B marketing. It's eliciting this strong emotional response that grabs your audience's attention, gets them talking about your brand, and makes your content rise above the noise. What's not to love? So in this episode of Remarkable, we're talking about a group of filmmakers who know about shock factor: Traktor. And with the help of our special guest, Senior Director, Head of Content at Domino Data Lab, Yuval Zukerman, we chat about going for the shock factor, creating a series of ads for a single campaign, and using your constraints to your advantage.About our guest, Yuval ZukermanYuval Zukerman is Senior Director, Head of Content at Domino Data Lab. He joined the company in July 2022 as Director of Technical Alliances. Prior to Domino Data Lab, Yuval served as Manager of Partner Marketing at VMWare. He has also founded his own company, Enavigo, LLC, focusing on technology leadership for results-oriented marketers, mobile development, developer relations, translation and localization project management.Over his career, he has served in a variety of roles across the technology lifecycle. From developer and engineer to project manager, technology consultant to technical creative and sales. He has worked with non-profits, medium-sized clients and global Fortune 500 companies in verticals that included CPG, B2C and B2B services, hospitality and financial. Past clients include Four Seasons Hotels and Resorts, Wells Fargo, Verizon Wireless, The Coca-Cola Company, Philips, and The Greater Boston Food Bank. He has a B.S. in Computer Science from UCLA and an M.A. in Information Technology and Software Engineering from Harvard.About Domino Data Lab​​Domino Data Lab powers model-driven business for the world's most advanced enterprises, including over 20% of the Fortune 100. Their Enterprise MLOps platform speeds up the development and deployment of data science work while increasing collaboration and governance, to scale data science into a competitive advantage. Their platform enables thousands of data scientists to develop better medicines, grow more productive crops, adapt risk models to major economic shifts, build better cars, improve customer support, or simply recommend the best purchase to make at the right time. Domino is backed by leading venture capital firms: Sequoia Capital, Bloomberg Beta, Coatue Management, Dell Technologies Capital, Highland Capital Partners, In-Q-Tel, and Zetta Venture Partners.About TraktorTraktor is a group of filmmakers based in Venice, California. Though they're based in California, they're originally from Sweden and met in film school: Directors Sam Larsson, Pontus Löwenhielm, Patrik von Krusenstjerna, Ole Sanders, Mats Lindberg and producer Richard Ulfvengren. They're globally renowned for their work serving brands like Lego, Nike, PepsiCo, Levi's, Fox Sports, MTV, Virgin Atlantic, and more. They've done music videos for Fatboy Slim, the Flaming Lips and Madonna. At least twice they've been recognized as the most award-winning directors in the world. They've won three Grand Prix at Cannes, a handful of Clios, an Emmy, and been nominated for a Grammy.What B2B Companies Can Learn From Traktor:Go for the shock factor. Knock your audience off-balance with unexpected content. It's inexpensive, it's attention-grabbing, it's memorable. Traktor made ads for Fox Sports that showed a man high diving straight into the ground, and another of two blindfolded men swinging oversized bats at each other. They were shockingly off-kilter for sports ads at the time. Over 20 years later, Yuval still remembers them vividly. Ian says, ”I wouldn't say Traktor's first ads are cheaply done, but comparatively. Like, there's no celebrities. They're just jarring. It's just taking the one salient point that you want the person to know and finding some sort of crazy connection out there in the ether to drive that home and then make the audience think, make them laugh.”Create a series within your campaign. It gives you a format while allowing you freedom to play on your theme. So you can create a variety of ads that appeal to different audience preferences, increase brand awareness and recall, and even run A/B testing with them. It's like how Traktor made a series of ads for Fox Sports. Ian says, ”They have one ad on an Indian sport and one on a Chinese sport. As the viewer, you realize it's a series. It becomes something that's beyond just a one-off thing. And now people think they're so funny. That's where ads are at their best, when it's less of a one-off and more of something that's ongoing.”Use your constraints to your advantage. Traktor leaned into their tight budget, and in doing so created stylized videos that were visually unique and captivating. Ian says, “If you don't have the budget, how can you steer into doing things grainier or less quality or unintentionally done in that way?”Quotes*”You're taking time from your audience. Traktor is giving them an exchange: Humor. They're giving you a giggle and a laugh. And that is something that is very important for us, to provide people value for their time. Time is the most valuable thing we have. So you better not waste it.” - Yuval Zukerman*”No matter what your goal is in the content, stand up to your core set of values. Live up to those standards. I always tell my kids, ‘Are you proud of what you just did?' Because there's something you can feel good that you delivered or created.” - Yuval ZukermanTime Stamps[0:55] Meet Yuval Zukerman, Senior Director, Head of Content at Domino Data Lab[2:08] Why are we talking about ads by Traktor?[3:01] What does Yuval's work at Domino Data Lab entail?[5:35] Learn more about Traktor[12:07] What makes ads by Traktor remarkable?[20:35] What are marketing lessons we can take from Traktor?[27:58] What's Yuval's content strategy?[33:02] How does Yuval get leadership buy-in on new content?[41:40] How does Yuval prove the ROI of content?LinksCheck out films by TraktorConnect with Yuval on LinkedInLearn more about Domino Data LabAbout Remarkable!Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both non-fiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Senior Producer). Remarkable was produced this week by Jess Avellino, mixed by Scott Goodrich, and our theme song is “Solomon” by FALAK. Create something remarkable. Rise above the noise.

Domino Data Lab was founded in 2013 and was one of the early innovators in the space known as MLOps. It delivers a unified platform for enterprises to build, deploy, and manage machine learning workloads and provides access to data, tools, compute, models across any environment. Domino enables ML and data teams to collaborate, ensure governance, and reduce costs at scale. In this episode, Wei Lien Dang chats with Chris Yang, co-founder and CTO of Domino Data. Join us as we discuss:  00:00 Introduction to Domino Data Lab 02:04 The Origin Story of Domino Data Lab 06:58 Balancing Authenticity and Customer Feedback 10:52  Open Communication and Collaboration Among Co-founders 14:24 The Importance of Engaging with Users 16:19  Building the First Feature: Enabling Compute Workloads in the Cloud 22:42 Discovering Unexpected Use Cases and Features 33:20 The Importance of Choosing a Target Market 35:44 The Evolution of MLOps and the Current AI Wave  Sandhya Hegde is a General Partner at Unusual Ventures, leading investments in modern SaaS companies with a focus on AI. Previously an early executive at Amplitude, Sandhya is a product-led growth (PLG) coach and mentor. She can be reached at sandhya@unusual.vc. Wei Lien Dang is a General Partner at Unusual Ventures and leads investments in infrastructure software, security, and developer tool.  Wei was a co-founder of StackRox, a cloud-native security company prior to its acquisition by Red Hat. He can be reached at wei@unusual.vc. Chris Yang is the co-founder and CTO of Domino Data.  Unusual Ventures is a seed-stage venture capital firm designed from the ground up to give a distinct advantage to founders building the next generation of software companies. Unusual has invested in category-defining companies like Webflow, Arctic Wolf Networks, Carta, Robinhood, and Harness. Learn more about us at https://www.unusual.vc/.

Dr. Kjell Carlsson, Head of Data Science Strategy & Evangelism at Domino Data Lab, contemplates AI's impact on human intelligence and its implications on understanding ourselves. We also discuss ways that artificial intelligence's value is underestimated in society and its less-known applications. Key Takeaways:  How AI can help us actually boost human intelligence, and what we've learned about our own limitations through advancements with technologies like generative AI Making use of unstructured data and the biggest possibilities AI can offer us now The future of generative AI and the influence of open-source technology  A roadmap to making applications with generative AI more accessible Resources, individuals and brands to help you extend your comprehension of AI Guest Bio: Dr. Kjell Carlsson is the Head of Data Science Strategy and Evangelism at Domino Data Lab where he advises organizations on scaling impact with AI. Previously, he covered AI as an industry Analyst at Forrester Research, and he is the host of the Data Science Leaders Podcast. ---------------------------------------------------------------------------------------- About this Show: The Brave Technologist is here to shed light on the opportunities and challenges of emerging tech. To make it digestible, less scary, and more approachable for all! Join us as we embark on a mission to demystify artificial intelligence, challenge the status quo, and empower everyday people to embrace the digital revolution. Whether you're a tech enthusiast, a curious mind, or an industry professional, this podcast invites you to join the conversation and explore the future of AI together. The Brave Technologist Podcast is hosted by Luke Mulks, VP Business Operations at Brave Software -  makers of the privacy-respecting Brave browser and Search engine, now powering AI with the Brave Search API. Music by: Ari Dvorin Produced by: Sam Laliberte  

This is Episode 27 of the Thinkdata podcast in partnership with Dataworks and on today's show, I welcome Ahmet Gyger to the show.Ahmet is the Senior Director of AI product at Domino Data Lab and has a wealth of experience in product management which has seen him work for the likes of Apple, Microsoft & Salesforce. Ahmet leads AI product development at Domino, the Enterprise MLOps platform trusted by over 20% of the Fortune 100.We spoke about:His background and what made him leave the relative comfort of "Big Tech" to join Domino.Who is Domino and what makes them and their platform uniqueThe steps Domino Data Lab takes to ensure that its platform and technology remain current and can continue meeting the needs of the AI practitioners that use the platformHow does Domino Data Lab address the ethical considerations and potential biases that can arise during the AI model development processAhmet's thoughts on what makes a great Data product, and what considerations should companies make before looking to build out a dedicated AI product team.What new tools and features can we expect to be released by Domino over the next 12-18 months?

James Alcorn is a partner at Zetta Venture Partners, one of the world's leading AI focused VC investors, serving technical founders building AI-native companies. Zetta has partnered with the founders of Kaggle, Domino Data Lab, Nabla Bio, and other leading franchises.In this episode #135 w James Alcorn, learn about:Given the extensive focus on AI and it's exponential mainstream growth, I wanted to bring on someone who understands this space from various angels, the technological shift, business models, venture capital investment opportunities and what founders should know.A couple of weeks ago, I contacted my network and one name that kept coming up was James Alcorn so here we are. James is a partner at Zetta Venture Partners, one of the world's leading AI focused VC investors since their inception in 2015.This episode can be best described as an introduction to AI, whilst being a deep dive into various aspects. I asked James about Zetta's origin story given they've been 100% focused on only AI since 2015, his thoughts on AI today vs 5 years ago, what exactly led to ChatGPT becoming mainstream, open source vs priopetiary models, infrastructure vs application layer and more.And yes, we cover a lot of questions that founders and investors are asking today including IP protection, metrics for investment, valuation, geographical differences and we end with a special rapid fire final sprint, make sure to listen through for this special conversation.It's now time to explore your curiosity. Please enjoy!(Timestamps below)***Follow us on Instagram, LinkedIn or TwitterContact us via our website to discuss sponsorship opportunities, recommend future guests or share feedback, we love hearing how to improve! Thank you for rating / reviewing this podcast on Apple Podcasts and Spotify, it helps others find us and convince guests to come on the show! ***

 ---> Check out the Causeartist Partners here.---> Subscribe to the Causeartist Newsletter here.In this episode of the Disruptors for GOOD podcast I speak with Kimberly Shenk, Co-founder and CEO of Novi, on connecting brands, manufacturers, and suppliers to help launch sustainable products faster.Technology is fundamentally changing the way we live, work, connect with each other and with the outside world. Current breakthroughs are challenging every sector in every country, with a speed and depth previously unseen.  Now, more than ever, the emergence of new technologies has the potential to transform our lives, make the planet a better place, and transform our path from unsustainable to sustainable.No one knows better how to harness the power of technology for the greater good than Kimberly. Her mission in life is to replace unsustainable products with sustainable ones, a way of life she reflects in both her home and business. When Kimberly became pregnant, her constant questioning of chemical ingredients in beauty and personal care products culminated in a tech company start-up, Novi,  a groundbreaking platform for researching, sampling and sourcing raw ingredients and packaging that has enabled a new era of sustainable, transparent consumer products.Today, Novi's vast dataset and Artificial Intelligence has had a hand in the formulas and packaging of thousands of beauty and personal care products that are clean and sustainable, and better for people and the planet.Kimberly's earlier days saw her graduating top of her class from the United States Air Force Academy with a double major in Mathematics & Operations Research.  When stationed at Pearl Harbor as a Captain in the Air Force with a highly coveted Top Secret security clearance and working directly for the Pacific's 3-star General running intelligence models, she won the 2013 Richard H. Barchi Prize for her work in “Analytics on Improving Integrated Air & Missile Defense in the Pacific Area of Responsibility.”She then attended graduate school at the Massachusetts Institute of Technology on a full-ride scholarship to study Data Science, and graduated with a Master's Degree in Operations Research.After the Air Force, she started a second career in the tech industry. She built and led the Data Organization at Eventbrite and led the Product Organization at tech startup Domino Data Lab. With a passion for clean products, she co-founded NakedPoppy in 2017, raising $4 million to use data to bring clean and sustainable products to market. ---> Check out the Causeartist Partners here.---> Subscribe to the Causeartist Newsletter here.Listen to more Causeartist podcast shows hereFollow Grant on Twitter and LinkedInFollow Causeartist on Twitter, LinkedIn, and Instagram

Today we have special guest Jennifer Fu, a software engineer who specializes in front end development.  Currently working at Domino Data Lab, she comes with 20 years of experience in many programming languages, but loves React!  Much of her recent work is with Web3.js and Three.js.  We discuss how to work with the 3D JavaScript library, and discuss how easy it is to use.  As a bonus, we get some of her insight to excelling during the coding interview process.  Make sure to check out her extensive library of articles at medium.com (link below).Sponsors Top End Devs Raygun | Click here to get started on your free 14-day trial Coaching | Top End Devs Links Jennifer Fu - Medium Jennifer Fu - Cupertino, California, United States | Professional Profile | LinkedIn Modeling - blender.org GitHub: Where the world builds software Three.js - JavaScript 3D library Picks Jennifer- Baltimore Museum of Art TJ- TMNT Shredder's Revenge Paige- Watch The Great British Baking Show | Netflix Official Site

What does it take to turn the latest advances in AI into products that deliver business impact at Walmart levels of global scale? Srujana Kaddevarmuth is the Senior Director of Data & Machine Learning Programs at Walmart Global Tech. Her team drives data strategy and grapples with data science productization every day. With millions of employees, hundreds of millions of customers, and petabytes of data at any given moment, Walmart offers some unique lessons in the complexities of building teams, processes, and products to effectively leverage AI at scale. In this episode, Srujana shares a few of those lessons, along with her perspective on nonlinear career paths, organizational collaboration and alignment, and her ongoing fascination with what's next. Plus, she dives into her passion for fostering diversity in data science and tech, sharing strategies leaders can implement to help bring more women into the field. We discuss: What to prioritize when experimenting with next-gen tech How to use “communities of practice” to align your organization Solving governance, reproducibility, and knowledge sharing challenges at scale Bringing more women into data science  In this season finale episode, host Dave Cole also shares his three biggest takeaways from his many in-depth conversations with leaders in data science. Stay tuned for a whole new season of Data Science Leaders coming soon! We're just getting started. Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

What do flossing your teeth and combatting application security vulnerabilities have in common? We find out in this session of the HIP Podcast, featuring IAM expert Daniel Stefaniak, Senior Product Manager for Identity and Access Management, Domino Data Lab. A former premier field engineer for Azure AD and program manager on the Microsoft Identity product team, Daniel talks with Sean about how a focus on performance over security paves the way for application breaches, combatting multifactor authentication (MFA) fatigue, and how Azure AD handles MFA.

There's tremendous value in pure data science research. In an enterprise context, however, it all comes down to how learnings and insights from that research can help advance business growth, customer experience, and product innovation. Sunil Kumar Vuppala is the Director of the Global Artificial Intelligence Accelerator at Ericsson. His career journey from a researcher role to data science leadership has given him years of perspective on how ML professionals and their business side counterparts can build partnerships that pay off in both the near and long term. In this episode, Sunil shares some of those key lessons on education, communication, and collaboration. Plus, he details a unique MLOps strategy he's employed to address challenges with scaling model monitoring. We discuss: How a research background can inform leadership style MLOps best practices for scale Forming mutually beneficial partnerships between business stakeholders and data science teams Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Large enterprises will always have some internal groups that are more change-averse than others. But progress often necessitates change, and how well you navigate the change management process can make or break your success as a leader. Michal Levitzky is the Head of Data & Analytics (CDO) at Migdal Group, a leading insurance and finance company in Israel. Michal has spearheaded the introduction of data and analytics functions at multiple organizations, and she knows a thing or two about negotiating the complexities of change management during analytics transformations. In this episode, Michal shares her advice for AI leaders driving meaningful change at their own companies. Plus she details her philosophy on structuring data and analytics teams for maximum efficiency and collaboration. We discuss: Using experience in fields like accounting as building blocks for leadership in data science Change management during model-driven transformations A structure to enable BI and data science functions to better support each other  Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Welcome to Reimagining Company Culture, a series discussing emerging trends and priorities shaping the future of workplace culture and employee wellbeing. We highlight thought leaders who are constantly evolving their strategy and can provide insight to folks about how to address new business challenges.  AllVoices is on a mission to create safe, happy, and healthy workplaces for all, and we're excited to learn from experts who share our mission.In this episode of Reimagining Company Culture, we're chatting with Michelle Arieta, Chief People Officer at Domino Data Lab. Michelle delivers tangible results to an organization from the human capital side by attracting, hiring and retaining top-tier talent. She is a strategic business partner who approaches organizational issues with a systematic approach to support performance within an organization. Tune in to learn Michelle's thoughts on the best way to train, resource and invest in People managers, proactively preventing burnout, innovating in the People space, and more!About AllVoicesIn today's workforce, people often don't feel empowered to speak up and voice their opinions about workplace issues, including harassment, bias, and other culture issues. This prevents company leadership from making necessary changes, and prevents people from feeling fulfilled, recognized, and included at work. At AllVoices, we want to change that by providing a completely safe, anonymous way for people to report issues directly to company leaders. This allows company leadership real transparency into what's happening in their companies—and the motivation to address issues quickly. Our goal is to help create safer, more inclusive companies.

Without a clearly defined methodology, complex projects with multiple technical and business stakeholders often fall apart. The risk is especially high when trying to scale data science work in an enterprise organization.  That's why David Von Dollen, Head of AI at Volkswagen of America, integrated agile methodology with CRISP-DM to help his team navigate roadblocks and accelerate progress on the path to model deployment. He shares how this hybrid approach enables his team to be more strategic about project lifecycles, unlocking real business impact even faster.  Plus, David provides advice for building relationships with key business stakeholders and shares his philosophy on using the art of data science to benefit humanity.  We discuss: Implementing hybrid CRISP-DM and agile methodologies Building relationships with stakeholders across the business Using data science to solve challenges outside of work       Mentioned during the show: DataKind      Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.  Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Even with the recent rise of specialized data science degree programs, top-notch data science talent can come from anywhere.  Those in leadership positions have a duty to share their knowledge and support aspiring data scientists, regardless of the unique path that brought them to the field.  Sidney Madison Prescott, Global Head of Intelligent Automation (RPA, AI, ML) at Spotify, has made a habit of sharing her expertise and giving back. And in the process, she's built a personal brand that would inspire future leaders in any industry.  In this episode, Sidney shared her career story, offered advice for building diverse data science teams, and detailed her work in robotic process automation at Spotify.  We discuss: Sidney's career journey and her guidance for women and people of color in data science How a strong personal brand can open doors to opportunities in tech Why data science leaders should care about robotic process automation   Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.  Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Model governance is vital, especially in heavily regulated industries like insurance. Strong governance can help ensure that key models are reproducible, explainable, and auditable—all important factors for both internal model development workflows and for external regulatory compliance. But the best governance strategy isn't always obvious. Anju Gupta, VP Data Science & Analytics at Northwestern Mutual, is a big believer in establishing model governance practices early, and she shares her thoughts on the topic in the episode. Plus, she talks about some surprising roles on her data science team and the unique value that comes from pairing actuaries with data scientists. We discuss: How to establish scalable model governance practices The intersection of actuarial work and machine learning Roles you didn't know you needed on your data science team Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

In every industry, people consume data. They work to understand what it can tell them in order to make smarter decisions. But the nature of data in the world of life sciences presents some unique challenges—and opportunities—for data science. In this episode, Sidd Bhattacharya, Director of Healthcare Analytics & AI at PwC, dives deep into these dynamics and shares his perspective on how leaders can operationalize AI at life sciences companies. Plus, we talk about the role data science has played in the fight against COVID-19 and the remarkable effort to develop such highly effective vaccines. We discuss: How data science in life sciences compares to other industries Operationalizing AI and measuring the ROI Strategic recommendations for data science leaders AI's contribution to the fight against COVID-19 Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Many people assume that once you establish a manufacturing line, the hard work is done and things remain relatively static. The reality, especially in electronics manufacturing, is entirely different. Constantly changing data streams and endlessly dynamic variables present some unique challenges for data scientists in the field. But there are lessons on data sharing, model adoption, and real-time impact that ML professionals in any field can learn from. In this episode, Alon Malki, Senior Director of Data Science at NI (National Instruments), opens a window into the world of data science in electronics manufacturing. Plus, he shares why human-in-the-loop processes are essential to gaining buy-in for AI in the enterprise. We discuss: Data science in electronics manufacturing Strategies for sharing data to improve manufacturing processes Human-in-the-loop applications Looking for challenge-motivated data science talent   Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

When your data science team is consistently more reactive than proactive in addressing business challenges, it can be difficult to be seen as strategic partners. But by prioritizing building business domain expertise and always asking about the “why” behind any request, you'll start to build a rapport and change the nature of the relationship. In this episode, Indy Mondal, Senior Director of Data Science, AI & Product Insights at DocuSign, explains how to create strong business partnerships to earn data science a critical and strategic seat at the table. Plus, he shares his unique perspective on the business impact of models and why self-service tools are essential to delivering value. We discuss: How to use data science to inform business strategy Using models to drive efficiency across the organization The role of self-serve tools in data science  Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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To create sustainable business value, data scientists need to navigate all the elements of what this episode's guest has dubbed “the data science and analytics value chain.” So what are those elements? And how can you ensure you hire and develop the team that delivers on each one with every single data science project? Nancy Hersh, Chief Data Officer at Arcadia, joins the show to break it all down. We discuss: Five elements of the data science and analytics value chain How an apprenticeship model can bring data scientists closer to the business Unique hiring strategies in an ultra-competitive market Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

The coding, models, and experiments inherent in data science work may have more to do with understanding human well-being than you think. Machine learning and AI can be applied in ways big and small to further our understanding of human behavior—and influence our well-being. Takuya Kitagawa, Chief Data Officer & Managing Executive Officer at Rakuten Group, believes there must be a shift toward focusing on well-being when it comes to how brands relate to customers. He joins the show to share his perspective on the future of data science, plus he details his approach to managing a large team spanning many products, cultures, and geographies. In this episode, we discuss: The role of ML in unifying the customer experience across multiple products Managing globally distributed data science teams Understanding human intention and well-being with technology Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Bias is an ever-present enemy of sound data science in healthcare. Without proactive measures to mitigate bias in the data used to build and train models, real people can bear the brunt of potentially life-altering negative consequences. Vikram Bandugula, Senior Director of Data Science at Anthem, knows this issue intimately from his extensive experience in healthcare. He joins the show to share his perspective on bias, plus he details his approach to fostering employee motivation and positive team morale. In this episode, we discuss: Problem-solving in data science and healthcare Managing bias in healthcare data sets and models Motivating high-performing employees and teams  Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Is your team mining all available data to inform your business strategy and grow revenue? Is your company prepared to compete against others who are? If you're like most, the answer is probably no. How can you future-proof your organization and take steps toward an autonomous enterprise? Janet George is an enterprise AI leader and author with experience across companies including Oracle, Apple, Accenture, Yahoo!, eBay, and more. She joins the show to discuss the meaning of autonomous enterprise and the process required for true transformation. We discuss: What is an autonomous enterprise? Where are companies falling short in their data transformation? The investment and first steps required on the transformation journey How to prioritize data projects for a larger impact on revenue Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Who uses the models that we create and how do they use them? Those key questions underpin the notion of responsible AI.  Since algorithms can have a significant societal impact, it's vital that data scientists are aware of the broader context in which they may be applied.  In this episode, Anand Rao, Global Artificial Intelligence Lead at PwC, breaks down why responsible AI should be an important consideration for every data science team. Plus, he explains what you need to be successful in AI consulting, and why a portfolio approach to ROI is the best way to demonstrate value to the business.  We discuss: The difference between AI in the 1980s and today Why data science leaders should care about responsible AI The ingredients for an effective data science consulting practice ROI analysis in data science  Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.  Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

When highly disruptive events like the COVID-19 pandemic occur, data science teams may have to throw historical data out the window. Models trained on what happened in the past simply don't work in a radically different present. In this episode, Karin Chu, VP Data Science and Digital Analytics at Peapod Digital Labs, discusses how her team is tackling that challenge head on, particularly as the global supply chain crisis impacts sectors from grocery to apparel. Plus, she explains why two things are so vital to the success of a data science team: ML engineers and a culture of communication. We discuss: How data science teams are navigating the supply chain crisis The vital role of an ML engineer Tips for communicating about data science in business Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Legal work may not be an obvious application of data science to many advanced analytics leaders. But that should change. In this episode, Peter Geovanes, Head of Data Strategy, AI & Analytics at Winston & Strawn, breaks down the nuts and bolts of legal analytics and how it's revolutionizing the way law firms win new business—and cases. Plus, he shares insight on the types of legal challenges data science can help address inside any organization. We discuss: The role of advanced analytics in the legal sphere Use cases on both the business and practice sides of law How analytics leaders and general counsels can work together What's next in the world of legal analytics   Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture without getting bogged down in the minutiae of the day to day? Jan Neumann, Executive Director, Machine Learning at Comcast, leads a team of about 300 data scientists, divided into eight different focus areas. If anyone knows how to manage a large data science team, it's him. In this episode, he shares his strategies for effectively managing a team of this scale in the enterprise. Plus, he explains why he prioritizes continued learning, and shares tips for building out a feature store. We discuss: - Managing large data science teams at scale - Making time to gain knowledge from the ML community - What a feature store is and why data scientists should care Mentioned during the podcast: - The Idealcast with Gene Kim - Mik + One with Mik Kersten - a16z Podcast - Yannic Kilcher on YouTube Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As compute capability continues to expand, the banking industry is turning more and more to data science to enable better customer experiences. Use cases have proliferated, from product recommendation engines to predictive customer retention alerts. These innovations can drive real business value, but managing the rollout of process and technology changes always presents interesting challenges. In this episode, Chun Schiros, SVP, Head of Enterprise Data Science Group at Regions Bank, reveals how her team is leveraging AI solutions to optimize the banking experience. And with insight applicable to data science leaders in any industry, she shares her change management tips for driving adoption of machine learning among data skeptics. We discuss: - How data science use cases have evolved in the banking industry - AI solutions in banking that optimize the customer experience - Change management tips for winning over data science skeptics Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Should your team patent its data science work? With open source such an important part of the data science community, patents almost seem antithetical to the ethos of the field itself. But it turns out, there are some very good reasons to pursue data science patents in business. In this episode, Kli Pappas, Associate Director of Global Analytics at Colgate-Palmolive, shares his team's process for deciding whether to patent an algorithmic process—and what benefits it can bring. Plus, he talks about why a statistical background is so important for teams that generate data. We discuss: - The transition from getting a PhD in chemistry to the analytics world - Finding the balance between statistical and computer science backgrounds - Why you should patent your data science work and how to do it Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

There are many ways to structure a data science function in a global enterprise. But what's been the winning strategy for global technology distributor Ingram Micro? Creating a data science “nerve center.” Centralizing data science talent has helped elevate analytics at Ingram Micro to better solve complex business problems using machine learning and AI. In this episode, Tim Suhling, VP Global Business Intelligence at Ingram Micro, explains how it all happened, and what data science leaders everywhere can learn from the transformation. Plus, he shares his perspective on how data science can impact “Customer 360” programs and different approaches to measuring the success of models. We discuss: - The relationship between data science and business intelligence - Embarking on a customer 360 initiative - Measuring the effectiveness of data science Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

To embed models into SaaS platforms at scale, it pays to have a cross-functional team—software engineers, UX designers, data scientists, machine learning engineers—all working together. That collaboration allows you to tackle hard challenges around scaling models to work across hundreds of thousands of customers. And it enables you to build something that offers tremendous value across many different use cases. Jayesh Govindarajan, SVP Data Science & Engineering at Salesforce, joins the show to share how his team makes this a reality. Plus, he talks about the priceless value of customer feedback and the three areas where data science teams should focus their efforts. We discuss: - Arriving at data science from a pure engineering background - Why telemetry is no substitute for customer feedback - Tips for embedding models into a SaaS product - The three pillars of work for a data science team Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As episode four of five in our series on enterprise culture change for AI adoption, we speak with Nick Elprin, CEO of Domino Data Lab. His company has currently raised nearly a quarter of a billion dollars to bring their ML ops platform to some of the largest enterprises around the world. In this episode, Seth breaks down some of the models for AI maturity that Domino uses when they assess enterprises. Nick also shares his unique perspectives on AI maturity as both the leader of a fast-growing startup and as someone with hands-on experience working with well-established legacy enterprises. If you enjoyed this episode, be sure to tune in tomorrow for the final episode in this series. We have heard from some fantastic guests this week, including IBM, Facebook, and SAP leaders.

In healthcare, only 14% of scientific discoveries actually make it into clinical practice. But data science, in lockstep with the digital transformation, is helping to change that. As healthcare data and clinical studies transition to digital form, the opportunity to use data science and AI to generate insights and recommend treatment pathways is greater than ever. And the ability to make healthcare delivery more equitable is within reach. In this episode, Kaushik Raha, VP Data Science & Health Content Operations at Elsevier, explains how data science is transforming the healthcare industry. Plus, he shares his thoughts on bias and some best practices for operationalizing data science. We discuss: How data science is helping to modernize healthcare Working with clinical analytics to root out bias Advice for operationalizing data science   Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As business leaders become more educated on the value that machine learning can deliver, the demands on data science teams only become greater. Business stakeholders are now interested in much more than the accuracy of predictive models. They're asking questions about productionization, scalability, and bottom line ROI. In this episode, Nimit Jain, Head of Data Science at Novartis, joins the show to explain how this sea change is transforming how data scientists approach proof of value. Plus, he talks about how companies are adopting responsible AI practices and provides a window into the world of customer experience analytics. We discuss: - How proof of value has evolved over time - The principles of responsible AI - Customer experience analytics use cases Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Talent is pouring into data science, even though it always seems like there's not enough to meet demand. Learning opportunities for people getting into the field have exploded in just the past decade. That means standing out from the crowd—both as a leader and as a practitioner—has become more important than ever before. In this episode, Bob Bress, Head of Data Science at FreeWheel, explains how professionals at all levels can position themselves to win in a burgeoning market. Plus, he offers advice on how data science leaders can stimulate collaboration and intellectual curiosity within their organizations. We discuss: - How to stand out from your peers - Intellectual curiosity, innovation, and collaboration in large organizations - Being the CEO of the data science project you're working on Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

You may not have a formal “portfolio management” function within your data science team, but in all likelihood, you're executing some of its key components already. But being more intentional around portfolio management can pay big dividends. Without it, you could be missing out on a powerful and holistic way of demonstrating the value your team provides to the business. In this episode, Katya Hall, Director of Enterprise Analytics at McKesson, explains how the portfolio management process sets the groundwork for defining KPIs that track the actual value derived from predictive models and insights. Plus, she shares her thoughts on a process for validating model accuracy and managing risk. We discuss: - Tips for working with business counterparts - Data science portfolio management - Model risk management - Supply chain analytics Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Mike Foley has been building data science teams from scratch since before they were called “data science” teams. His perspective on questions like “Where do I start?” or “How do I get buy-in?” can help leaders growing data science teams of any size avoid some pitfalls along the way. Currently the Senior Director of Data Science at Hitachi Vantara, Mike joined Dave for a conversation that goes deep into the steps required to stand up a data science practice. Plus, he shared what inspired him to go back to school, and gave listeners a unique peek into the world of marketing analytics. This episode features Mike's insight on: Starting data science practices from scratch The complexities of marketing analytics The value of continuous learning Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Between GDPR, CCPA, and more regulatory frameworks on the horizon, the landscape of personal data—and how it can be used in business—is shifting. On this episode, John Thompson, Global Head of Advanced Analytics & AI at CSL Behring, joins host Dave Cole to discuss that shift, and a potential future in which we as individuals could be compensated for the use of our data. Plus, John shares the two types of analytics teams he's seen work well during his career as a data science leader. Topics covered include: - If and how individuals can know what companies are doing with their data - How GDPR and CCPA portend the future of data - Structuring, growing, and managing different styles of analytics teams Check out these resources mentioned during the show: - Analytics: How to Win With Intelligence - Building Analytics Teams Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

In a perfect world, healthcare data would always be strategically organized, up-to-date, and easily accessible—all in a patient-centered, privacy-first way. But the reality is much more complex. Robin Foreman, Director of Data Science at CVS Health, joins the show to discuss the challenging world of data science in clinical trials. She also explains how product analytics can be used on the back end of model implementation to answer the key question of “did it work?” Robin shared her perspective on: - Turning a PhD in public health into a career as a data science leader - Navigating data science and clinical trials - The life cycle of product analytics Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

People analytics—the application of data science and analytics in the world of HR—can provide valuable insights into recruitment, retention, and productivity. But when working with people's sensitive demographic, compensation, and performance data, ethical and privacy considerations must come first. In this episode, Adam McElhinney, Chief Data Sc ience Officer, VP of Data Insights at Paylocity , explains how his company approaches people analytics, and what all data science leaders can learn from the discipline. Plus, he offers a view into the hiring process Paylocity uses to add top-notch data science talent to its team. The conversation covers: People analytics and HR Data science in the hiring process Embedding data science into SaaS platforms Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Dave Frankenfield , VP Enterprise Data and Analytics at Optum , oversees a team of 2,700 data professionals. How do you structure a team of that size? What functions does it cover? And how does it collaborate with and deliver value to the rest of the company? In this episode, Dave discusses the strategies he's used to build his team, the lessons he's learned, and the advice he has for data science leaders scaling teams of any size in the enterprise. The conversation covers: - Building an analytics team from the ground up - Approaches to managing shadow IT - Tradeoffs between distributed vs. delegated data science teams Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Data plays a vital role in cancer treatment. In oncology analytics, data analytics can help identify promising treatment strategies, offer better access to affordable care options, and provide critical feedback to medical teams. In this episode, Susan Hoang , Vice President of Oncology Analytics at McKesson , shares how her team overcomes the inherent challenges of messy healthcare data to deliver insights that can help save lives. Plus, she shares the unique journey that took her from economics and marketing to data science. We discuss: - Susan's unique path to becoming a data science leader - Sifting through messy healthcare data - How to define measurable data science outcomes and gain buy-in Tune in on Apple Podcasts , Spotify , our website, or wh erever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Computer scientists can be fearless, pushing the limits of computational power and the scale of data we can analyze. On the flipside, statisticians can be intensely skeptical, always measuring error and bringing a critical perspective. According to Chris Volinsky, AVP - Data Science & AI Research at AT&T, it's these two schools of thought that combine to make data science such a powerful function in business. In this episode, Chris shares his thoughts on how computer science and statistics fundamentals can help us continue to push data science forward. Plus, he offers advice on how to conduct all-important exploratory data analysis (EDA) effectively. We discuss: - How computer scientists influenced data science - What statistical thought brings to the equation - Tips and tricks for doing EDA right Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Forward-thinking companies are already embedding machine learning into their business processes—and seeing the payoff of model-driven decisioning. But what about deep learning? How can ambitious data scientists get started with deep learning? How can they satisfy their own curiosity, and eventually apply new approaches to address real business challenges? The field may be more approachable than you think. In this episode, Eitan Anzenberg, Chief Data Scientist at Bill.com, offers his advice on getting started with deep learning. We discuss: -Testing, trusting, and understanding your data and your models -Advice for reducing bias in highly regulated industries -Considerations for getting started with deep learning -Challenges of deep learning as a discipline Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As a data scientist, you must be able to explain complex ideas in simple ways. Knowing your data, knowing the business, and presenting the data clearly to business stakeholders is an essential part of the role. Gaia Bellone, SVP - Head of Data Science at KeyBank, has a passion for leading and training her data science team. Her priority: ensuring that her team is successful at communicating data effectively. In this episode, we discuss: -Knowing your data and communicating it to the business -Where to begin when launching a data science program -International differences in data science Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Data science jobs outnumber data scientists by three to one. The industry is looking for ways to close that gap, including turning to the concept of the citizen data scientist. But in today's episode, Romain Ramora , Head of Data Science & Innovation - Supply Chain at Cisco , shares why he thinks we shouldn't be putting critical models in the hands of people lacking the proper expertise. Romain shared his perspective on: - How a background in risk analytics and consulting prepared him for a move into supply chain analytics - The effectiveness of the citizen data scientist - Who should lead a data science project Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

It's a common refrain among enterprise data science professionals: 70-80% of their time is spent on data wrangling and pipeline building. But what happens if you bring data science and data engineering together under one roof? Mark Teflian , VP, Data Science and Data Engineering at Charter Communications (Spectrum), joins the show to share how bringing the functions together can help increase efficiency and productivity for everyone at an enterprise scale. Mark covered: Why data science and data engineering should be under one roof How data science helped keep Americans connected when COVID-19 drove massive shifts in internet usage Different ways to approach embedding data science into production systems Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

In this episode, Logan Lyles, VP of Customer Experience at Sweet Fish Media, talks to Ryan Kelly, VP of Growth Marketing at Domino Data Lab & Author of Florence the Data Scientist and Her Magical Bookmobile. Resources we mentioned: - Naming Your B2B Podcast - Choosing The Host for Your B2B Podcast

In data science, experimentation is everything. But as a leader, how can you balance experimental work that may never pay off with delivering measurable business value every day? In this episode, Khatereh Khodavirdi, Global Head of Analytics & Data Science - Global Merchants at PayPal, talks with host Dave Cole about how she has navigated that balance throughout her career, all while building world-class data science teams in the process. We also discuss: - Making an impact with data science in the ads business at eBay - Getting buy-in for data science experiments - Hiring tips for leaders growing their organizations Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

The title of “Data Scientist” leapt into prominence in 2012 when the Harvard Business Review named it the “sexiest job of the 21st century.” Almost ten years later, what's changed? And what's next? In this episode, Dave Cole is joined by Mike Tamir , Chief ML Scientist and Head of Machine Learning/AI at SIG , to break down the shifting trends in data science, NLP, and ML—and what it all means for leaders in the field. The conversation covers: - The past, present, and future of data science - The different roles and responsibilities within a data science team - New and exciting advancements in NLP - When models are right for the wrong reasons For daily news and insights on all things data science, follow @MikeTamir on Twitter. Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Today's guest is Nick Elprin, CEO and Co-Founder of Domino Data Lab, an enterprise data science management platform. Domino has raised over $100M to date. Nick previously served as Senior Application Technologist at Bridgewater Associates, the largest hedge fund in the world. In today's episode, Nick shares his definition of a platform in the proper sense and speaks about the place and purpose of AI platforms. He also discusses why an enterprise would choose a platform and under what circumstances it would be a worthwhile investment while also sharing his idea of what the future will look like. If you are interested in reaching the Emerj audience, whether through thought leadership, e-mail, podcasts, or more, check out  emerj.com/ad1.

We're in the middle of the fourth industrial revolution. Industry 4.0 encompasses the use of advanced automation and analytics in manufacturing. So how is data science driving value in Industry 4.0? In this episode, Dave Cole is joined by Paul Turner, Vice President Industry 4.0 Applications & Analytics at Stanley Black & Decker, to break down everything you need to know. We discuss: -The definition and foundational pillars of Industry 4.0 -Three approaches to Industry 4.0 -Balancing data science and domain expertise to deliver value -Inspiring action through predictive analytics Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As more organizations recognize the power of data to transform their decision making (or for it to become a product in its own right), the role of the Chief Data Officer has become critical. So what are the biggest challenges facing every good CDO? And where do data science teams intersect with that work? In this episode, Dave Cole is joined by Heidi Lanford, Chief Data Officer at Fitch Group, to discuss strategies for cultivating partnerships between data science leaders and the Chief Data Officer. They also explored questions such as: What is the role of a CDO? What does “data as a product” mean? How can the CDO enable others to deliver insights? How do you start a data literacy program? What's the deal with “citizen data scientists?” Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Data scientists in the healthcare industry face some especially tough challenges. Not only do they have to contend with complex regulatory landscapes impacting the data they can work with, but they're also constrained by some less than modern processes. 75% of medical communication is still delivered by fax. And that's just one example. Derrick Higgins , Head of Enterprise Data Science & AI at Blue Cross and Blue Shield of Illinois, Montana, New Mexico, Oklahoma, and Texas, talks to us about the processes his team has put in place to overcome these unique challenges. In this episode, we cover: - The challenges of working in a highly-regulated industry - Implementing a continuous code review process - Where IT intersects with the data science life cycle Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

The field of bioinformatics plays a critical role in medical breakthroughs like the COVID-19 vaccine. Fiona Hyland , Director of R&D, DNA Sequencing Informatics at Thermo Fisher Scientific , teaches all of us about how it happened in the latest episode of Data Science Leaders. What we talked about: - A quick run through genetics and bioinformatics terminology - Bioinformatics, and the genetics of cancer and the coronavirus - The role of data science in the development of the COVID-19 vaccine Check out these resources we mentioned during the podcast: - COSMIC genetic database - The 1000 Genomes Project - gnomAD Genome Aggregation Database Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Listening on a desktop & can't see the links? Just search for Data Science Leaders in your favorite podcast player.

The best data scientists are continually learning something new, taking on unfamiliar projects, and keeping their skills fresh. The best leaders in the industry create a culture where teams have opportunities to grow and are able to clearly understand and communicate data science concepts. In this episode, Dave Cole is joined by Dr. Satyam Priyadarshy, Managing Director for India Center, Technology Fellow, and Chief Data Scientist at Halliburton, to discuss how to explain complex subjects in simple terms for better business outcomes. Dr. Priyadarshy also explained: - How his experience as a professor has influenced him as a leader in data science - What governance he puts in place for his training methodology - How to cultivate a diverse workforce Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Listening on a desktop & can't see the links? Just search for Data Science Leaders in your favorite podcast player.

Data science operationalization is a simple enough concept. But in practice it can be a complicated and often overwhelming challenge. In this episode, Dave Cole is joined by Nishan Subedi, VP, Algorithms at Overstock.com, to discuss the best way to operationalize data science. Nishan talked about: - The data science experts that make up the team at Overstock - Strategies to improve search and measure search success - The difference between ML engineers and ML scientists - How to design teams around product orientation Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Listening on a desktop & can't see the links? Just search for Data Science Leaders in your favorite podcast player.

Data science teams are responsible for delivering impactful models, of course. But they're also responsible for translating that impact (and all the work that goes into it) for business stakeholders of all kinds. In this episode, Dave Cole is joined by Nate Litton, Vice President, Data & Analytics at Toyota North America, to discuss strategies to strengthen the relationship between data science leaders and their business counterparts. They also talked about: - How to structure your organization for great partnerships - Cultivating your teams' soft skills alongside their technical skills - How to measure stakeholder engagement and partnership success - What to do with difficult partnerships Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

What if we could predict how long our models will last in the field? Is there a mathematical way to estimate mean time to failure for a specific model? In this episode, Dave Cole is joined by Celeste Fralick, Chief Data Scientist at McAfee, to discuss AI reliability and how it can help predict model decay. Celeste also explained: - What AI reliability measures - Processes to put in place to measure AI reliability - The difference between DevOps and MLOps at McAfee - How adversarial machine learning works - How to build out a more diverse data science team Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Listening on a desktop & can't see the links? Just search for Data Science Leaders in your favorite podcast player.

In this episode, Flavilla is joined by David Bloch. They discuss how not to fail your digital transformation. David Bloch is a New Zealand born technologist specialising in data science and artificial intelligence.  His track record spans twenty years in emergent technologies focusing on software development, digital activation, and adoption strategies to create compelling solutions to business problems. David recently joined Domino Data Lab as an evangelist to promote their enterprise-class data science platform.  David also partners with many startups providing advice, mentoring, and growth strategies based on his experience as a founder and consultant for technology companies.To connect with David, CLICK HERETo visit Domino Data Lab's website, CLICK HEREJoin Tech Brains Talk mailing list for more perks, CLICK HERETo find out more about 3 Colours Rule Agency, CLICK HERE

Kayla Cytron-Thaler is a Business Team lead of Domino Data Lab, a computer software enterprise that powers model-driven business for the world’s most advanced enterprises, including over 20% of the Fortune 100. After recently discovering that her passion lies in the world of sales and business development, Kayla has been a constant achiever when it comes to: building relationships with data science leaders to drive efficiencies, increase the competitive advantage, & save resources, helping IT leaders centralize data science to save time & resources, and giving data science teams the ability to be more productive. From starting her business development career in 2008 at Looker to most recently joining Domino Data Lab, her main role is to lead a team of 5 BDRs to help with onboarding, coaching, crafting creative outreach, and setting up meetings to build a pipeline. She’s also recently Co-Founded the Data Leaders Executive Lounge, an exclusive community of Data Science leaders who are looking for a space to learn, collaborate and discuss how decision science will elevate their organization and career. She’s helped nurture and grow the community rapidly in having over 150 members in less than a year. Find out more and reach out to Kayla Cytron-Thaler through her LinkedIn - https://www.linkedin.com/in/kayla-/Join the Sales Hustle Community! Text “Hustle” to 424-401-9300!If you’re listening to the Sales Hustle podcast, please subscribe, share, and we’re listening for your feedback. If you are a sales professional looking to take your sales career to the next level, please visit us at https://salescast.co/ and set a time with Collin and co-founder Chris.Please make sure to rate and review the show on Apple.

Ryan Kelly is a marketing leader at Domino Data Lab and author of the world’s first-ever children’s book about data science. But he didn’t write this children’s book to encourage the next generation to get passionate about future careers in data science. He also wrote it to inspire a passion for problem-solving. Ryan believes that our world is full of problems, and our future relies on the passion and willingness of future generations to solve them. He was also tired of trying to describe to his daughter what he did at work. I invited him onto the Tech Talks Daily podcast to learn more about the story behind Florence, the Data Scientist. Domino Data Lab powers model-driven business for the world’s most advanced enterprises, including over 20% of the Fortune 100. Its Enterprise MLOps platform speeds up the development and deployment of data science work while increasing collaboration and governance to scale data science into a competitive advantage. The platform enables thousands of data scientists to develop better medicines, grow more productive crops, adapt risk models to major economic shifts, build better cars, improve customer support, or recommend the best purchase to make at the right time.

Algorithms can have an outsized impact on society. That's why many data science leaders have focused a lot of effort recently on defining data literacy and ethics in a way that's operationalizable in their company culture. In this episode, Dave Cole speaks with Chris Wiggins, Chief Data Scientist for the New York Times, about why a foundation of data literacy and data ethics is so important for data scientists. What we talked about: -Building a data science team at the New York Times -Creating a data culture -The relationship between a data science team and a data analyst team -Necessary soft skills for data scientists Some resources mentioned during the podcast: hackNY Beautiful Data: The Stories Behind Elegant Data Solutions Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Listening on a desktop & can't see the links? Just search for Data Science Leaders in your favorite podcast player.

Unleashing the power of data science can help us solve some of the world's most important challenges. What's it like to be leading the way? That's just what Data Science Leaders, a podcast for data science teams that are pushing the limits of machine learning and AI, will explore. In this introductory episode, Lesley Crews, a producer at Sweet Fish Media, talks with Dave Cole, your show host and the Chief Customer Officer at Domino Data Lab. What they talked about: -Dave's background and experience in the data science world. -Why Dave wanted to start this podcast. -What listeners can expect to hear in each episode. Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. Listening on a desktop & can't see the links? Just search for Data Science Leaders in your favorite podcast player.

The practice of data science can improve any business, but there are key success factors, including quality data, an understanding of business processes, and a commitment to remain focused. One way to solve many of these objectives "out of the box" is to employ a data science platform. Register for this episode of DM Radio to hear host @eric_kavanagh interview several guests about the nature, possibilities and real-world practice of data science! He'll talk to Loretta Faluade of PROS, Josh Poduska of Domino Data Lab, and Shawn Rogers of TIBCO.

Kayla Cytron-Thaler is the Business Development Team Lead at Domino Data Lab. If you need tactical advice on how to book more meetings and get promoted as an SDR, this episode is for you. Kayla skips the fluff and gets straight into the strategies you need to hit your quota this quarter. Connect with Kayla here on LinkedIn. If you enjoy the podcast, would you please consider leaving a short review on Apple Podcasts? It takes less than 60 seconds, and it really makes a difference in helping to grow this show and find the best guests possible for you. Follow The Podcast: Apple/Spotify: Millennial Sales Twitter: TommyTahoe Instagram: TommyTahoe YouTube: TommyTahoe Website: Millennialmomentum.net

Kayla Cytron-Thaler is the Business Development Team Lead at Domino Data Lab. If you need tactical advice on how to book more meetings and get promoted as an SDR, this episode is for you. Kayla skips the fluff and gets straight into the strategies you need to hit your quota this quarter. Connect with Kayla here on LinkedIn. If you enjoy the podcast, would you please consider leaving a short review on Apple Podcasts? It takes less than 60 seconds, and it really makes a difference in helping to grow this show and find the best guests possible for you. Follow The Podcast: Apple/Spotify: Millennial Sales Twitter: TommyTahoe Instagram: TommyTahoe YouTube: TommyTahoe Website: Millennialmomentum.net

Kayla Cytron-Thaler books 85% of her meetings off video. She runs a clinic on personalization at scale and gives raw actionable video tactics.Four Actionable Takeaways:Zoom the camera into their Linkedin profile at the beginning of your videoNo notes in the connection request (Morgan Ingram also shared this one)Time block your research. No more than 3 minutes of time leading up to a video.Use the native LinkedIn videos instead of the cold email videos.Kayla Cytron-Thaler’s Path to President’s Club:ENT BDR, Domino Data LabENT BDR, LookerResources Discussed:Buckets: The Prospect Prioritization Technique created by Reisert ConsultingFocus Areas: ProspectingNick and Armand are almost above-average sales reps. They are horrible technicians.Which is why 30MPC’s go-to podcast editors are Resonate Recordings - reach out to caleb@resonaterecordings.com and mention 30MPC if you wanna edit your own podcast.

In this episode of AMP Up Your Digital Marketing, Glenn Gaudet speaks with Jason McClelland, CMO at Domino Data Lab. They talk about the importance of product-led growth in the SaaS industry and how to execute it successfully.   Product-led growth is a huge trend in marketing these days, but traditionally it works best for products that are fairly straightforward, like Netflix and Spotify. Leveraging product-led growth for a highly technical audience can be tricky, but it can pay off immensely. Jason says it helps him generate interest amongst developers and data scientists, who typically don’t care about flashy marketing initiatives — they just want to try the product out. Once a user has opted into a free trial or freemium product, you have to nurture them through the process. It’s like a Ferrari, Jason explains. You can’t just throw someone into the driver’s seat and say, “Go.”    

In Today's Episode I sit down with Kayla Cytron-Thaler, Enterprise Business Development Rep at Domino Data Lab. In part 2 of this 2 part series, we discuss her cadence that is getting massive results for her and her team at Domino! Don't miss out on this! Kayla brings the fire again!! Enjoy Check out Kayla and all her musings on LinkedIn: https://www.linkedin.com/in/kayla-/ Follow Me at: Linkedin : https://www.linkedin.com/in/nedarick/ Twitter : https://twitter.com/Ned_Arick

In Today's Episode I sit down with Kayla Cytron-Thaler, Enterprise Business Development Rep at Domino Data Lab. In part 1 of this 2 part series, we discuss the intricacies of sales in a post covid world, sales management, sales enablement and even how NOT to pick up girls. I'd say it's a pretty gnarly episode! Enjoy Check out Kayla and all her musings on LinkedIn: https://www.linkedin.com/in/kayla-/ Follow Me at: Linkedin : https://www.linkedin.com/in/nedarick/ Twitter : https://twitter.com/Ned_Arick Email : Ned@Halossystems.com.

Matthew Granade is Chief Market Intelligence Officer at Point72 and the Managing Partner of Point72 Ventures. Matthew oversees all proprietary research and data efforts at the firm, manages several of the internal systematic strategies, leads early stage venture investing, and recently launched Hyperscale, a new strategy that invests in AI-driven startups and connects them with operating companies to build model-driven businesses. Before joining Point72, Matthew started his investment career at Bridgewater and later was a co-founder of Domino Data Lab, a business that develops systems-of-record for enterprise data science teams across industries. Our conversation covers Matthew’s work optimizing the research process at Bridgewater, creating Domino Data, and leaving the company to join Point72. We turn to his tackling research and data science at Point72, blending the power of computers and humans, and overseeing Point72 ventures and Hyperscale. Learn More Read the Transcript Subscribe to the Capital Allocators Blog or Monthly Mailing List Don't Subscribe, but Let Us Know Who You Are Write a review on iTunes Follow Ted on twitter at @tseides Review past episodes of the Podcast

Jeff Gray brings 20 years of experience in sales, software delivery, engineering, operations and strategy. Most recently he was a VP of Business Operations at Domino Data Lab, a machine learning platform that focuses on the needs of enterprises with large teams of code-first data scientists. The company is funded by Sequoia and several other VCs. Prior to that, Jeff was a VP of Business Operations at CoreOS, a leader in the Kubernetes community that was acquired by Red Hat for $250M in Jan of 2018. In this episode we talk about scaling a startup from $1 to $10 million in revenue.

Kayla Cytron-Thaler is in Enterprise Business Development at Domino Data Lab. In this episode, we talk about how to stand out above the noise when you're prospecting. She's sent 250 personalized videos in the last few months and shares her process for using LinkedIn videos to generate 35%+ response rates and tons of meetings. Connect with Kayla on LinkedIn here and check out Domino Data Lab. Resources mentioned in the episode: Think Outside the Script Summer Virtual Tour Show Notes Page Reply Method Guide Become a Think Outside the Script Member

Chad sits down with Nick Elprin, the CEO of Domino Data Labs. You may think data science is all the rage, but it wasn't when Nick got started. In this episode, Chad and Nick discuss: Nick's career, some of his early times at Harvard and Bridgewater, and the lessons he learned through it all. How Nick got started doing what he's doing today. What it's like to build a company in a space that is emerging. Some persuasion techniques and tips for how to be persistent even when the going gets tough. How Nick landed Domino's first customers and why they held off on raising capital for some time. The mix of work that Domino is currently doing and what Nick's vision is for their future. — This episode of Mission Daily is brought to you by our friends at TriNet. TriNet makes HR easier, from payroll to benefits to compliance. AND they offer full-service solutions tailored to your industry and your company, whether your team is 10 people or 1,000. Check out TriNet today at trinet.com. — For full show notes and more, go to mission.org/missiondaily.

Cory and Brett sit down Domino Data Lab's Co-Founder and CEO Nick Elprin to hear about how Domino Data Lab is helping customers move from the traditional data driven approach and become more model driven.  Nick talks about the challenges in moving to a model driven approach and details the journey customers are taking to get there. Recorded from Domino Data Lab’s Rev2 Conference in New York City, Nick gives a brief history of Rev, Domino's yearly data science conference, and explains why this conference is different than the other AI and Data Analytics conferences out there. Lastly, Nick provides unique insights into some of the announcements that Domino Data Lab had at Rev2 and how the Domino Data Lab Platform is positioned to power model driven organizations. Music from this episode is by Andrew Belle.  Please go check him out...you'll thank us!

From the Google File System that paved the way to Hadoop, to Tensor Processing Units to Kubernetes, Google has been at the forefront of impactful technology innovation that has accelerated data analytics and AI.  Recorded from Domino Data Lab’s Rev2 Conference in New York City, Cory and Brett sit down with Rod Bodkin to get his perspectives on the state of AI in the enterprise.  Rod, who is a Technical Director in the CTO office for Google Cloud and is focused on Applied AI talks about some of the game changers that have led to the past evolutions in data science and what Google is doing to drive technologies to help further these evolutions in Cloud, AI and Data Science in the future.  The team also dives into some recent examples of how Google is applying Deep Learning in areas like YouTube and Google IO. The 2019 Disney Data & Analytics Conference is taking place at Disney’s Coronado Springs Resort in Orlando Floria on August 20th and 21st.  DDAC always has an all-star line up of speakers and this year Chesley B. “Sully” Sullenberger, III the “Miracle on the Hudson” Hero Pilot & Shane Battier Vice President, Analytics and Basketball Development for the Miami Heat will be the keynote speakers.  You can use promo code “BigDataBeard-2019” to receive 20% off your conference pass. Click here to register with the promo code.

I had the opportunity to attend the Rev2 Data Science conference held by Domino Data Lab. While not an IT conference, I attended so I can gain a better understanding on what businesses are doing with data.

Brett and Cory get the low down on Domino Data Lab’s Data Science Platform with returning guest, VP of Marketing Jon Rooney and their Deputy CTO, Ozzy Johnson.  We discussed what it means to truly be a platform and how to separate Domino from the pretenders.  Hear how the world’s most mature, model driven businesses leverage Domino to not only collaborate and accelerate model development, but also deliver simpler deployments that scale and provide reproducibility and explainability so critical for enterprises automating with AI.  Also hear how you can get a great discount to Rev2, the Domino sponsored Data Science Conference in NYC May 23-24.

Bloomberg Opinion columnist Barry Ritholtz interviews Matthew Granade, chief market intelligence officer at Point72 Asset Management. Granade directs the firm’s central portfolio and manages Point72 Ventures, which funds early-stage companies in financial technology, enterprise automation, artificial intelligence and cybersecurity. Before joining Point72 in 2015, Mr. Granade co-founded Domino Data Lab and served as co-head of research for Bridgewater Associates. 

Daniel Chalef is one of the few marketing and product professionals in this category. Originally from South Africa, he graduated from University of Capetown with a multidisciplinary degree is business and technology. After eight years of technical work as a developer in South Africa, Daniel founded sales acceleration vendor, KnowledgeTree, as CEO and CTO. Most recently, Daniel worked as the head of marketing at Domino Data Lab, a platform centralizing the work of data scientists. He is now working as a Contract CMO and marketing consultant for companies like Crunchbase, Mixpanel, and several early stage startups. The topics discussed in this episode include focusing on your questions and not just your data, not getting hung up on the numbers, and new data sources for analysts and marketers.

The future of data science with Eduardo Ariño de la Rubia