Podcasts about data science team

Welcome to an interview with the author of Winning with Data Science: A Handbook for Business Leaders. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals.  Akshay Swaminathan is a data scientist who works on strengthening health systems. He earned a degree in Statistics from Harvard University and is an MD candidate and Knight- Hennessy Scholar at Stanford University School of Medicine. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times. He currently leads the data science team at Cerebral. Get Akshay's book here: https://rb.gy/tievb8 Winning with Data Science: A Handbook for Business Leaders Here are some free gifts for you: Overall Approach Used in Well-Managed Strategy Studies free download: www.firmsconsulting.com/OverallApproach McKinsey & BCG winning resume free download: www.firmsconsulting.com/resumepdf Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo

Welcome to Strategy Skills episode 422, an interview with the author of Winning with Data Science: A Handbook for Business Leaders. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals.  Akshay Swaminathan is a data scientist who works on strengthening health systems. He earned a degree in Statistics from Harvard University and is an MD candidate and Knight- Hennessy Scholar at Stanford University School of Medicine. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times. He currently leads the data science team at Cerebral. Get Akshay's book here: https://rb.gy/tievb8 Winning with Data Science: A Handbook for Business Leaders Here are some free gifts for you: Overall Approach Used in Well-Managed Strategy Studies free download: www.firmsconsulting.com/OverallApproach McKinsey & BCG winning resume free download: www.firmsconsulting.com/resumepdf Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo  

Do you want to work in a fast-paced environment, improve your technical skills, and work on unique challenges? Our Data Science team may be a great fit for you!  Bradley Fay, Director of Data Science, and Sam Donnelly, Talent Acquisition Partner give us a glimpse into the innovative projects the team is working on and our interview process.  Our global Data Science Team at DraftKings adapts and improves decisions based on experimental work and builds models that helps advance our products or creates betting lines for our apps.  Learn more about what the team in London is doing by checking out our DraftKings Life Blog: https://careers.draftkings.com/draftkings-life-blog/engineering/3-reasons-to-join-the-data-science-team-at-draftkings/  If you're ready to apply, click here: https://bit.ly/3VHwdgS Follow us on ➡️ Linkedin: https://www.linkedin.com/company/draftkings-inc-/ Instagram: https://www.instagram.com/draftkingslife/ Twitter: https://twitter.com/draftkingslife Facebook: https://www.facebook.com/draftkingslife  

Listen to the full episode on Anchor, or in your favorite podcast distribution platform! Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Starting from scratch We've talked about a lot of aspects of data science on this podcast — building software features, conducting research, learning new methods and skills, recruiting new members — but there's one we've always avoided: building a new team from the ground up. A large reason for that is personnel — while your cohosts may be intrepid, they are not experts in this area. This month, we bring on two people who are: Eric Silberstein and Ezra Freedman, who founded the Data Science team at Klaviyo. We draw on their wealth of experience, knowledge, and lessons learned the hard way while founding a young team. As you might expect, these lessons extend beyond data science teams in particular — whether you're founding another team or starting a new business, or looking to join a team in its early stages, you might be able to learn from our discussions, such as: How setting concrete goals is key for a new team How to think about your first hire, and your next five How to steer a team through large organizational changes while maintaining its culture and essence “When you view the world, do you think of it as ‘if-then' statements, or do you tend to think of it as some sort of function to optimize? Our team needed both.” - Eric Silberstein, VP of Data Science Read the full writeup on Medium!

In dieser Podcastfolge von MY DATA IS BETTER THAN YOURS spricht Jonas Rashedi mit Kevin Indig, den er auf der OMR kennengelernt hat. Kevin war zuletzt für Unternehmen wie Shopify und Atlassian tätig und arbeitet nun als Growth Marketer. Sein Schwerpunkt ist die strategische Beratung im Bereich SEO, die er z.B. bei Snapchat und Riverside umsetzt. Der Unterschied zu Paid-Kanälen ist bei der Suchmaschinenoptimierung, dass die Daten schlecht in Echtzeit und überhaupt messbar sind. Denn das Ranking bei Google und damit das Ausspielen auf Basis von Keywords basiert auf Rankingfaktoren, die wir nicht genau kennen. Zudem besteht ein zeitverzögerter Feedbackloop, da man die Ergebnisse erst später sieht – denn Crawling, Indexierung und Ranking brauchen Zeit! Kevin hatte bei Shopify allerdings ein eigenes Data Science-Team – ein sehr ungewöhnlicher Ansatz für diese Form des Marketings, doch er konnte hierdurch große Erfolge erzielen. Die Daten rund um Crawling und Indexierung wurden gemessen und so aufbereitet, dass Signale von Google direkt sichtbar waren. So wusste das Team, ob es auf dem richtigen Weg ist. Nötig hierfür ist es, die komplette Seite auseinanderzunehmen und jeden Bestandteil zu verstehen: Wo sind Wachstumstreiber, wo kann man die Performance forecasten und wo der Ablauf in Echtzeit gestalten? Die nötigen Tools hat Kevin mit seinem Team alle selbst gebaut. Jonas und Kevin geben noch einige Tipps, wie man mit Management und Stakeholdern umgehen sollte, um Ressourcen für solch ein Projekt zu erhalten. Zum LinkedIn-Profil von Kevin: https://www.linkedin.com/in/kevinindig/ Zur Webseite von Jonas: https://www.jonas-rashedi.de Zum LinkedIn-Profil von Jonas: https://www.linkedin.com/in/jonasrashedi/ Zum Newsletter von Jonas: https://share-eu1.hsforms.com/1m5SesP8QStuLDLjrJAnZXAfcb4n Zum Podcast auf Spotify: https://open.spotify.com/show/1CbTKaUuWhWnCUjGEagajs?si=6d7feac78076476d Zum Podcast bei Apple: https://podcasts.apple.com/de/podcast/my-data-is-better-than-yours/id1524294960 Zum YouTube-Kanal: https://www.youtube.com/c/JonasRashedi

Today's guest is Eilon Baer, Data Science Team Lead at Explorium. Explorium provides the first External Data Platform that automatically discovers thousands of relevant data signals to improve analytics and machine learning. They are on a mission to reshape the way organizations access external data to build their unique competitive advantage. Explorium's External Data Platform empowers data scientists and business analysts to acquire and integrate third party data efficiently, cost-effectively and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail and e-commerce can increase revenue, streamline operations and reduce risks. In the episode, Eilon will talk about: His background and current role with Explorium, Building their external data cloud, Benefits that the platform brings to customers, An insight into the day-to-day life of the data team, Plans for growth and career opportunities at Explorium, and What it's like to work as a customer-facing data scientist

While leading a mature data science function is a challenge in its own right, building one from scratch at an organization can be just as, if not even more, difficult. As a data leader, you need to balance short-term goals with a long-term vision, translate technical expertise into business value, and develop strong communication skills and an internalized understanding of a business's values and goals in order to earn trust with key stakeholders and build the right team. Elettra Damaggio is no stranger to this process. Elettra is the Director for Global Data Science at StoneX, an institutional-grade financial services network that connects clients to the global markets ecosystem. Elettra has over 10 years of experience in machine learning, AI, and various roles within digital transformation and digital business growth. In this episode, she shares how data leaders can balance short-term wins with long-term goals, how to earn trust with stakeholders, major challenges when launching a data science function, and advice she has for new and aspiring data practitioners.

Welcome to Mining Your Own Business Podcast. Each episode will bring in data and analytics gurus from around the world as they regale us with their data analytics stories and enlighten us with their secrets for how to turn data into actionable insights.In this podcast, we are joined by Korri Jones, A Senior Lead Machine Learning Engineer and Innovation Coach at Chick-Fil-A. He discusses his background and his career journey that lead him to be a Machine Learning Engineer working at Chick-Fil-A.he also talks about what it is to be a Machine Learning Engineer and what are the usual works and tasks every day. He also shares how he manages his teams such as the Data Science Team and the Data Engineering Team to be more productive and dynamic with each other. He also answered the big question in this show, given an ideal scenario, what would you focus on? Lastly, Korri describes what it looks like working with Chick-Fil-A.Key TakeawaysWhat is a machine learning engineer and when they get involved in a projectThe value that an innovation coach brings to a teamWhere project ideas originate & how they are decided uponHow they operationalize projects at Chick-fil-AThe unique culture of the Chick-fil-A team QuotesI want people to do what they are hired to do and really just crush it. I don't need a data scientist doing all of the engineering work. I don't need the engineer doing all the data science work. - Korri Some people talk about thinking outside the box. We help to make sure that there is no box in the first place. And so you don't have to try to think inside or outside of a box. - Korri Featured in this EpisodeEvan Wimpey Director of Analytics Strategy at Elder ResearchLinkedin: https://www.linkedin.com/in/evan-wimpey-40469b47Website: www.elderresearch.comKorri Jones Senior Lead Machine Learning EngineerInnovation Coach at Chick-Fil-A Linkedin: https://www.linkedin.com/in/korri-jones-mba-780ba56 Chapters00:00 Introduction01:39 Korri's Background04:00 Korri's Career as a Senior Lead Machine Learning Engineer07:01 Balancing the technical side and business side09:01 What is an innovation coach?11:36 Getting the ideas from the data science team or others14:02 When does a machine learning engineer get involved16:44 How are projects decided upon19:11 If there was a magic "analytic success" button, where would Korri use it?22:51 Working With Chick-Fil-A 25:10 Outro

This week, Karen is joined by Natalia Lyarskaya, Chief Data Officer at ZestMoney. With a background in applied economics, Natalia made her career in the financial technology sector as a data scientist and later on, as a data leader building data science teams and capabilities. In this episode, she talks about her excitement at working with start-ups and being at the foundation of something new, as well as of her experience building data science teams from scratch. Not only will you hear about the challenges she overcame building and leading distributed teams, but you will also get her insights on how companies can get the most value from their data scientists, and her best tips as to how to align data science work with business objectives while ensuring data scientists and business leaders understand each other.   Enjoy! ********************************* Show notes  ********************************* Natalia's LinkedIn: https://www.linkedin.com/in/nlyarskaya/

In this episode of The Next CMO podcast, we speak to John Wall, partner and head of business development at Trust Insights, a marketing data consultancy helping organizations who don't have their own data science team with all things marketing data.He is also the producer of Marketing Over Coffee, a weekly audio program that discusses marketing and technology with his co-host Christopher S. Penn, and has been featured on iTunes. Notable guests include Chris Brogan, David Meerman Scott, Simon Sinek and Seth Godin. More info about John hereMore info about Trust Insights hereMore info about Marketing Over Coffee hereMore info about Plannuh hereMore info about The Next CMO podcast here  Produced by PodForte

In episode 45, my latest guest is Peter Sueref. Peter has worked with data his whole career since graduating in Computer Science at Cardiff University. As the Data Science Director at Centrica he worked on some of the biggest problems in energy today. While at Centrica he also launched their first Blockchain project, set up the first group-wide Data Science Team, built their Digital Accelerator Hub and set up the first Knowledge Transfer Partnership around data innovation. This year, he left Centrica to set up a Data Science startup, Empirisys, where he is the CTO and co-founder. In our conversation we explore Pete's career story, how he leapfrogged from Data to Data Science and the power of honesty & transparency as a data leader. We even hear how Pete put this into practice with Bring Your Own Data days at Centrica. I hope you hear some tips to inspire your data leadership development.

In this episode, Jason talks to Natalia Lyarskaya, Chief Data Officer at ZestMoney, and one of the DataIQ top 100 most influential people in data. Their conversation focuses on building data science teams, how data is measured, and how to apply a business and product outcome-focused approach to it. Natalia also shares her role and experience as a Chief Data Officer, her set of principles in building out a data science team and the cultural challenges in different markets.

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.

Simon Jones, Head of Data Science and Advanced Analytics at Saga, talks about building a remote-first data science team to help Saga recruit the talent it needs to modernize and engage its increasingly digitally savvy audience Providing seamless digital customer experiences wasn't always a priority for British ‘over 50s' insurance specialist Saga. But as a new cohort of digitally savvy consumers enter their middle ages, the firm's attitude toward the need for modernization has changed. As Saga Head of Data Science and Advanced Analytics Simon Jones explains in this week's Business of Data podcast episode, the company is now reimagining itself in light of the changing needs of its customers. “A lot of people are moving into the ‘over 50s' category, which is where Saga's footprint begins, and they don't necessarily think of themselves as the sort of person who signs up with Saga,” he says. “Trying to understand exactly how we can penetrate into that demographic group was a really important thing. “And what [we] recognized very early on was that a lot of it was down to our relationship with technology.” Saga is now developing new technological capabilities with these customers in mind, and Jones believes embracing a ‘remote-first' model for data science will give the company an advantage as it pursues this aim. The Benefits of Being Remote-First Jones joined Saga's insurance arm in May 2021 with a remit to build a data science team to help the company get the most out of its data asset. He says the role is the first he's held that has empowered him to truly embrace remote working. Jones argues that this approach makes it easier for Saga to recruit top-quality talent and makes a career at the company more attractive to data scientists who enjoy the flexibility that comes with remote working. “I've got a recruitment function right now, to build out a remote-first team, trying to find top talent in data science and bring them on board to Saga,” he explains. “That means our talent pool is anywhere in the UK.” “If somebody wished to explore a bit more of the country by basing themselves in different spots over the course of a working month, I have no problems with that,” he adds. “As far as I'm concerned, you're always working in the same location: The cloud, online, with me. “That makes it possible for us to reach out to talent which, for particular reasons, have based themselves outside the areas we'd normally be recruiting in.” What's Next for Data Science at Saga In the near-term, Jones' priorities include building out his team, helping Saga build out its data lake and sourcing new “exotic” datasets to provide staff with insights they don't have access to currently. But looking to the future, he sees his priorities shifting toward helping to drive the adoption of data-driven technologies across the organization and creating processes that help his team get data science products into production efficiently. “It's all part of serving the broader agenda of helping Saga advance,” he concludes. “There's going to be an awful

Today's guest is Puneet Gangrade, Data Science Team Lead at MightyHive in New York. Founded in 2012, MightyHive is the leading data and digital media consultancy that helps marketers take control. MightyHive delivers sustained results from the ground up through advisory for business transformation, privacy-first data strategy and digital media services. MightyHive is trusted by brands like Autodesk, Bayer, Electrolux, Renault, Sony, Sprint, TransUnion and US Bank to name a few. Puneet manages and implements clients' global portfolios for their complete digital transformation. His responsibilities include building end-to-end data warehousing systems in different cloud platforms (GCP, AWS, Azure), using the privacy-safe platform (Google Ads Data Hub) for KPI-driven analysis, utilizing the advertising, on-site analytics, and other data to look beyond the numbers, and bringing machine learning into action for all his clients. In today's episode, Puneet tells us about: Helping companies build an effective digital transformation strategy, Challenges the Data Science team are solving in industry, The importance of working effectively with stakeholders, How new data privacy laws will impact their offerings to customers, What he enjoys most about working at MightyHive & Exciting opportunities within the team

El Consejo de Transporte Público pidió a la SUTEL bloquear las aplicaciones tecnológicas de transporte y a la Procuraduría General de la República -en su condición de abogado del estado- demandar a las empresas encargadas de operar esas plataformas. Tal vez en otro entorno, la “noticia” hubiese dejado estupefactos a los ciudadanos. En nuestro régimen de libertades constitucionales, en la democracia plena que somos -admitámoslo- nadie se tomó muy en serio la petición. Ahora bien, hablando en serio, entendamos que el desarrollo tecnológico suele ir a una velocidad tan acelerada que normalmente las regulaciones van muy por detrás. No solo en Costa Rica sino a nivel mundial, la irrupción a mediados de la década anterior de las denominadas plataformas colaborativas de transporte, entre ellas Uber y DiDi, han venido a poner en jaque los modelos de transporte público convencionales. Los Estados muchas veces no han sabido cómo lidiar con este choque de dos mundos. La realidad es que, con el pasar de los años, este modelo de transporte público sustentado en la digitalización de la vida misma, se abrió un espacio en nuestras sociedades. Y en este punto en particular, el país tiene la tarea pendiente: por años el asunto tan espinoso como era no se supo enfrentar, así que se optó por dejar que los modelos coexistieran sin una legislación clara al respecto -pero- eso sí incluyendo cada vez más a las plataformas tecnológicas en otras normativas, como la tributaria. Con la cuestionable solicitud del CTP de bloquear estas aplicaciones -todo un gazapo jurídico para cumplir con una concesión sectorial- se vuelve a poner en el tapete público la necesidad de afrontar con soluciones políticas reales y no prohibiciones absurdas, la regulación de estos y otros nuevos servicios de la vida digital. En Hablando Claro conversamos del tema con Monserrat Guitart, abogada especialista en la materia y fiscal de la Cámara de Tecnologías de la Información, CAMTIC, y con Sebastián Urbina, ex viceministro de Transportes y actual Vicepresidente de Artificial Intelligence, Machine Learning and Data Science Team.

We talked about: Dat's career so far and the startup he co-founded (Priceloop) Who to hire first in a data team How to hire the first data scientist And many other things! You can find Dat on LinkedIn: https://www.linkedin.com/in/dat-tran-a1602320/ Join DataTalksClub: https://datatalks.club

In this episode, Michael Misiewicz talks about building a data science team, while breaking down what data science is, what his framework for it is, and giving insight into the many lessons he’s learned from his journey in building these teams. He offers advice to people who are looking to a build data science team and people who are looking to improve their existing team. Michael also shares his candid thoughts on organizational structure in data science teams. Contact Michael Misiewicz | Follow us on LinkedIn

Meet: Connan has a strong background in Economics with his Ph.D. and has also taught at UCLA as a professor. He was a Data Scientist at Uber for over 4 years before taking over the Data Science team at Just Auto Insurance. What you'll learn: Hiring for your Data team - a balanced team for success Future of Data Scientists - evolving requirements and technical skills Customer Lifetime Value (LTV) - an example from Uber If you have any questions for Connan, please feel free to reach out to him via LinkedIn: https://www.linkedin.com/in/connan-snider-4249311b/

In this episode, I talk to Chu-Cheng, CDO at Etsy. We cover his battlescars related to recruiting and building a Data Science team. At Etsy, Chu-Cheng leads the global data organization responsible for data science strategy, AI innovation, machine learning & data infrastructure. Prior to Etsy, Chu-Cheng led various data roles at Amazon, Intuit, Rakuten, and eBay. Chu-Cheng is a Ph.D. in computer science, with published papers in key AI/ML conferences.

Meet: Peter has held management-level positions at Hearst and Freestar, before landing at Teachable, where he is the Head of Data. He also has his MBA from NYU. What you'll learn: Building your data science team - case study and lessons learned 3rd party applications - pros/cons and the debate, to build in-house or buy If you have any questions for Peter, please feel free to reach out to him via LinkedIn: https://www.linkedin.com/in/peter-jaffe-4b18a66/

Why data science team communication is so difficult. Analytics Translator is not the solution. Role of PM in a data intensive solution team. Why you shouldn't rely on everyone's notes. What to do when you receive a long text. When to put things in writing and when not to. Handling difficult conversations.

Data science has increasingly become an integral part of the technical makeup of organizations. However, most CTOs don’t have experience building a data science component from scratch. Jon Morra got his start in data science at eHarmony when most technical teams did not have any researchers. He joins Peter today on CTO Connection to offer advice on how to build a research team from the ground up.Jon discusses why you want to bring on researchers who are generalists and why it is imperative that you have a clear problem you want to solve before assembling a team. He also explains why you should look at problems as oracles and offers guidance on the hiring process for data scientists. Listen to get ideas on starting a research team for your organization. (01:26) - Jon's path to becoming Chief Data Scientist at ZEFR(02:40) - Balancing competing priorities at eHarmony(04:30) - Where to start building up a data science team(08:16) - Approaches for making the first hire(11:14) - Beyond full stack(13:07) - Filling the gaps(16:18) - Thirsty to learn(17:56) - Promoting to manager(19:41) - Minimizing business risk(22:16) - Defining success(24:11) - Where should the team live within an org?(27:42) - Initial goals and expectations(29:32) - What is senior?(32:16) - Managing senior data scientistsSpecial thanks to our global partner – Amazon Web Services (AWS). AWS offers a broad set of global cloud-based products to equip technology leaders to build better and more powerful solutions, reach out to aws-cto-program@amazon.com if you’re interested to learn more about their offerings.CTO Connection is where you can learn from the experiences of successful engineering leaders at fast-growth tech startups. Whether you want to learn more about hiring, motivating or managing an engineering team, if you're technical and manage engineers, the CTO Connection podcast is a great resource for learning from your peers!If you'd like to receive new episodes as they're published, please subscribe to CTO Connection in Apple Podcasts, Google Podcasts, Spotify or wherever you get your podcasts. If you enjoyed this episode, please consider leaving a review in Apple Podcasts. It really helps others find the show.Podcast episode production by Dante32.

Meet John Morra who is the Chief Data Scientist at Zefr. He's a member of the LA CTO Forum and organizer of the LA Machine Learning Meetup. Three key takeaways from this episode:: Team management - give your team full trust and they will give you trust Teamwork - how to make sure you have the right resource balance on your team Hiring - what to look for when making a data scientist hire Bonus tip Founder's view - there is a lot more to starting a company than just coding - learn about how to run a business too

¿Cómo nace la relación entre psicología y economía? ¿Qué idea novedosa plantea la economía conductual? Además de la psicología, ¿la economía conductual se nutre de otras disciplinas? ¿Qué aplicaciones tiene la economía conductual y en qué campos? ¿Cuál es el futuro de la economía conductual? Para conversar sobre estos asuntos tuvimos como invitado a Marco Carrasco, magister de Investigación en Economía y Gestión, con mención en Economía y Psicología, por la Université Paris 1 Panthéon-Sorbonne — egresado en 1er puesto: Summa Cum Laude—, y Bachiller en Economía por la Universidad Nacional Mayor de San Marcos. Sus principales áreas de interés e investigación son la economía conductual y las políticas públicas. Se ha desempeñado como consultor y analista de la Organization of American States (OAS) de esta en su sede principal de Washington, DC y de Institutos de Investigación en Shanghai. Trabaja para el Ministerio de Desarrollo e Inclusión Social (Perú) y ha sido docente en la Universidad Nacional Mayor San Marcos. Es Co-Fundador e investigador del Instituto ASIA, así como Director-Fundador e investigador de BEST: Behavioral Economics & Data Science Team. Obtuvo el segundo puesto en el I Concurso de Ensayos de Investigación organizado por el Observatorio América Latina - Asia Pacífico (2015); y el primer puesto —categoría Indonesia— en el V Concurso de Investigación organizado por la Red Peruana para Estudios del Asia Pacífico - REDAP (2012).

Dan Costanza, Chief Data Scientist at Citi, joins me for the first episode in the new “Bitesize Insights for Data Driven Leaders” Series. Dan opens the show by explaining how he got interested in data. After graduating from college, Dan went into an investment banking role. Eventually, he received an exciting project that got him started down the data path. Dan says as someone who didn’t study computer science in school, it has been a heavy lift trying to get those technical skills up to par. During a code-heavy project, Dan needed to learn how to break up the project and work through it. Also, he learned how to think about sampling data without bias. Then, Dan explains the importance of emotional intelligence for data science. Conscientiousness and emotional intelligence are the things that you can actually interview for. Instead of judging people on their grades, we need to judge people on their ethics, communication skills, and willingness to work in teams. In India, Dan set up a data science team. The talent in India is insane. However, there are cultural differences Dan needed to work through. For instance, he told his team that they needed to speak up when they had ideas. If you create space for people to bring their own thoughts, you’ll hear loads of good suggestions. Before Dan told his team that, they would withhold useful information. Quotes: “When you look at the hiring research again, like there are two real categories and the one is things you don’t interview for, which are the intellectual horsepower things and those are - how smart you are, do you have some specific skills I need. The word that always comes up on the other side is conscientiousness, and that encompasses the stuff we talked about at the beginning, and the emotional intelligence, teamwork parts of it and those are the things you do actually interview for. Which is counterintuitive for a lot of people who work in quanti type roles because you want to ask people really hard questions, to see if they are smart, but the problem is the data doesn’t support that as being predictive of anything when you control for their grades.” “You start by spraying things around, working with a lot of people, just to get the volume in and see who those people are and meet people, and as you work a little bit, you start to understand their own types of workflow.” “More powerful then compliant is having good ethics there on the ground.” Read the full episode summary here: Episode #124 Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

Our guest this week, Madhu, works at one of the top 10 Australian banks. He has a lot of experience applying AI in customer service. Madhu speaks about how to grow an AI team, when to hire data scientists, when to hire someone to lead those data scientists, and what kinds of talent needs to be there to help guide data scientists.  If you're interested in learning more about our AI Opportunity Landscape work in the financial services industry, watch our video at emerj.com/fs1.

Meet Trevor Baldwin from Baldwin Krystyn Sherman Partners, an independent agency in Tampa, FL. Trevor explains how his internal data science team gives him a competitive edge with clients, helps him run a leaner agency and has deepened his relationships with  carriers.

One of our recurring themes on this podcast is the application of data in the real world and how data science is adapting to the needs of the businesses it serves. This episode is a perfect example of that. Jorge Lozano leads the data science team at Steelcase. Steelcase was founded over a hundred years ago and is the largest office furniture manufacturer in the world. So, what does a company like Steelcase do with data and data science? Listen and find out.

Scott Friesen’s transformation into a data analytics professional wasn’t exactly linear. After graduating with a biology degree and becoming a pre-med student, he switched gears and managed artists in the music industry. After that, he worked at Best Buy, eventually becoming their Senior Director of Analytics for the company’s consumer insights unit. Today, Scott is the SVP of Strategic Analytics at Echo Global Logistics, a provider of technology-enabled transportation and supply chain management services. He also advises for the International Institute for Analytics. In this episode, Scott shares what he thinks data scientists and analytics leaders need to do to become a trustworthy and indispensable part of an organization. Scott and I both believe that designing good decision support applications and creating useful data science solutions involve a lot more than technical knowledge. We cover: Scott’s trust equation, why it’s critical for analytics professionals, and how he uses it to push transformation across the organization Scott’s “jazz” vs “classical” approach to creating solutions How to develop intimacy and trust with your business partners (e.g., IT) and executives, and the non-technical skills analytics teams need to develop to be successful Scott’s opinion about design thinking and analytics solutions How to talk about risk to business stakeholders when deploying data science solutions How the success of Scott’s new pricing model was impeded by something that had nothing to do with the data—and how he addressed it Scott’s take on the emerging “analytics translator” role The two key steps to career success—and volcanos

Dr. Alaa Moussawi joins us to talk about how the NYC Council is leveraging data and how this data is helping New Yorkers along the way. NYC makes their data available so you too can explore it and answer the questions that you care about – https://opendata.cityofnewyork.us/ To learn more about Alaa’s Data Science Team, Read More ...

In today's episode, Jennifer Drabble, Director of Product & Data Science at Barclays, will talk more about creating Product & Data Science Team and how to build multi-skilled team in large and diverse organisation like Barclays. Get a FREE copy of our Product Book HERE

Kjersten Moody joined State Farm in July 2017 as Vice President and Chief Data & Analytics Officer in Bloomington, Illinois. Previously, Kjersten led Data & Analytics and IT groups at global companies, such as FICO (Braun), Thomson Reuters and Unilever. She has a record of delivering tangible, positive business results, and a depth of experience in scaling operations, planning/executing mission-critical business initiatives, and achieving profitability objectives. Kjersten is a graduate of the University of Chicago and has a proven track record in modernizing and scaling operations, executing mission-critical business initiatives, and achieving profitability objectives. An energetic leader with a focus on people development, diversity, and inclusion Kjersten demonstrates the ability to effectively lead and work in highly complex environments. In this episode, Kjersten talks about her love for data and how it compliments an understanding of human behavior. She is incredibly grateful for the chances others took on her to get her in the role she is today. Understanding how to thrive in stressful situations is one of the essential lessons Kjersten learned in her early roles. Her leadership style is open, honest, and collaborative while always ensuring to take time out of her day to serve others. In the healthcare industry, Kjersten gets to see her work through and enjoys the process of continuous improvement. Building teams have not changed much, some methods of work differ and where the work is performed. For example, information security has grown significantly to evolve with the ever-changing advancements in technology. Later, Kjersten explains how she builds a team, what diversity means, data strategy, data governance, and financial impacts. In This Episode: • [00:20] About Kjersten Moody • [04:45] Love for data • [06:40] Transition to technology consulting • [09:50] Lessons learned early on • [13:15] Leadership took the time • [14:40] Kjersten’s leadership style • [15:35] Transition to healthcare • [18:00] Lessons learned in consulting • [20:00] Building teams • [22:15] Qualifications for individuals • [29:10] Data strategy • [33:00] Data governance • [38:00] Understanding the business aspects • [45:20] Financial impacts • [48:20] Listener questions Some of Kjersten's quotes from the episode: “Challenges are a constant in a domain such as data science.” “Diversity is an attribute of the team. It’s the diversity of experiences, culture, and thought.” “The process of matching price to risk is inherently done through data.” “Data strategy is interpreted in many different ways.” “The leader needs to be able to work in a trusted way with business leaders and general managers.” Now you can support Data Futurology on Patreon! https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

In this episode, I talk about data scientists and ways you can attract the best talent to your team. Instead of telling your employees what they can do better, make them curious as to what they could do better. Then, I reveal the three things to look for when analyzing your pool of applicants. Once you have your team, now what? Once you have a decent pay settled, I explain the three things you will need to have for a capable team. Later, I tell you the elements, as a manager, you should be doing as rarely as possible. In This Episode: • [02:45] How to attract data scientists to your team? • [04:45] The three things to look for from your pool of applicants • [07:05] Adversity; test how they would react • [11:00] Three things needed to run an effective team • [18:00] Managers should be doing this as rarely as possible Creating a Data Team Session Quotes: 1. “Create a learning environment and continually challenging projects to focus on their development.” 2. “People should be open-minded and willing to learn; I test this in two different ways.” 3. “A lot of people come with technical skills from other countries.” 4. “They had to code it live with about eight people watching them, no pressure!” 5. “You know the answer, and you want to tell them to get to the outcome quickly. That’s an urge you have to roll back and fight against.” 6. “Purpose is really what gets us out of bed every day.” 7. “Make yourself redundant as quickly as possible.” Resources Mentioned: Drive: The Surprising Truth About What Motivates Us Connect: Twitter - https://twitter.com/datafuturology Instagram - https://www.instagram.com/datafuturology/ Facebook - https://www.facebook.com/datafuturology Support Data Futurology on Patreon! https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

Loka Podcast with Ameen Kazerouni, Head of the Data Science Team at Zappos by Bobby Mukherjee

In this #DataTalk, we talked with Marcelo Pimenta, Experian’s Head of DataLabs in Latin America, about ways to build a great data science team. Marcelo Pimenta earned his Bachelor of Science degree in Physics from Universidade de São Paulo and his M.B.A. from Fundação Getulio Vargas.

Recruiting data scientists has never been more competitive. Listen to Dave Evans - contributing-discoverer of the Higgs boson - to find out what he looks for during the recruitment process at FreeAgent. They're hiring!

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on. We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies. Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

In this podcast, Venu Vasudevan(@ProcterGamble) talks about the best practices of creating a research-led data-driven data science team. He walked through his journey of creating a robust and sustained data science team, spoke about bias in data science, and some practices leaders and data science practitioners could adopt to create an impactful data science team. This podcast is great for future data science leaders and practitioners leading organizations to put together a data science practice. Timeline: 0:29 Venu's jouney. 11:18 Venu's current role in PNG. 13:11 Standardization of technology and IoT. 17:18 The state of AI. 19:46 Running an AI and data practice for a company. 22:30 Building a data science practice in a startup in comparison to a transnational company. 24:05 Dealing with bias. 27:32 Culture: a block or an opportunity. 30:05 Dealing with data we've never dealt with before. 32:32 Sustainable vs. disruption. 36:17 Starting a data science team. 38:34 Data science as an art of doing and science of doing business. 41:37 Tips to improve storytelling for a data practitioner. 43:30 Challenges in Venu's journey. 44:55 Tenets of a good data scientist. 47:27 Diversity in hiring. 50:50 KPI's to look out for if you are running an AI practice. 51:37 Venu's favorite read. Venu's Recommended Read: Isaac Newton: The Last Sorcerer - Michael White http://amzn.to/2FzGV0N Against the Gods: The Remarkable Story of Risk - Peter L. Bernstein http://amzn.to/2DRPveU Podcast Link: https://futureofdata.org/venu-vasudevan-venuv62-proctergamble-on-creating-a-rockstar-data-science-team-futureofdata/ Venu's BIO: Venu Vasudevan is Research Director, Data Science & AI at Procter & Gamble, where he directs the Data Science & AI organization at Procter & Gamble research. He is a technology leader with a track record of successful consumer & enterprise innovation at the intersection of AI, Machine Learning, Big Data, and IoT. Previously he was VP of Data Science at an IoT startup, a founding member of the Motorola team that created the Zigbee IoT standard, worked to create an industry-first zero-click interface for mobile with Dag Kittlaus (co-creator of Apple Siri), created an industry-first Google Glass experience for TV, an ARRIS video analytics and big data platform recently acquired by Comcast, and a social analytics platform leveraging Twitter that was featured in Wired Magazine and BBC. Venu held a Ph.D. (Databases & AI) from Ohio State University and was a Motorola's Science Advisory Board (top 2% of Motorola technologists). He is an Adjunct Professor at Rice University's Electrical and Computer Engineering department and was a mentor at Chicago's 1871 startup incubator. About #Podcast: #FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future. Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/ Want to sponsor? Email us @ info@analyticsweek.com Keywords: #FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

If you are not a marketer or work with marketing folks, you are probably not aware of the importance of authenticity. Companies are constantly challenged with finding ways to connect in a genuine and meaningful way. Some brands, like Nike and Apple have mastered the art of authenticity when it comes to representing their brands. The marketing landscape is evolving more now and quicker than it ever has before, and the customers are getting smarter and aware of the products around them. You can't just throw dollars at your brand... you have to connect with customers. Today I sit down with the Head of Data and Analytics at PMG, Dustin Engel and we walk through the recent history of Beats and how it went up against the big boys and eventually became one of them. We talk about the difference some companies make and why they understand how to make that connection better than others. Apple has spent less money on advertising for their phones and iTunes than their competitors, and they have focused that money on experience. Store experiences. How do you make the store a part of the sales process? Apple has a unique way of making the shopping experience authentic, Apple has figured out to charge people premium prices and have created a luxury brand that is being consumed by the masses. They have bucked traditional trends of focusing on premium customers with higher margins and leaving the lower margin customers behinds. If customer experience is about delivering a consistent experience, then many companies have proven that. Where do these leaders fear disruption? What does Apple see on the horizon? What are they seeing around the next corner? SHOW NOTES Dustin Engel is the leader of the Advanced Media Team (data activated media) and the Analytics and Data Science Team at PMG Advertising Agency. In this episode we talk about his journey from aspiring Marine Biologist to a career in Digital Marketing. We get his perspective on many aspects of digital disruption, including beacons, Apple's next big move, airline fee's and late fee's.  We talk everything from Dr. Dre and Trent Reznor to Angela Ahrendts the Sr Vice President of Retail at Apple who used to be the CEO at Burberry. We talk Lebron, Neymar and Winning Moments That Matter with Beats by Dre headphones. Check out episode 109 for the deep dive with Dustin on Blockbuster Please subscribe and leave a review. www.wordsofmassdisruption.net   Instagram   |   Twitter   |   LinkedIn

This week on AI in Industry, we speak with Equifax's Dr. Rajkumar Bondugula about how the dynamics, composition and requirements of the data science team have evolved over the years. Raj also shares valuable insights on how to build a robust data science and machine learning team, use its collective intelligence to solve problems, and retain the team by engaging them with the right problems they expect to solve. For more insights from AI executives, visit: TechEmergence.com

Dustin Engel is the leader of the Advanced Media Team (data activated media) and the Analytics and Data Science Team at PMG Advertising Agency. In this episode we talk about his journey from aspiring Marine Biologist to a career in Digital Marketing. We get his perspective on many aspects of digital disruption, including beacons, Apple's next big move, airline fee's and late fee's.  We talk everything from Dr. Dre and Trent Reznor to Angela Ahrendts the Sr Vice President of Retail at Apple who used to be the CEO at Burberry. We talk Lebron, Neymar and Winning Moments That Matter with Beats by Dre headphones. THIS EPISODE Eric Hanes and Dustin Engel Check out episode 109 for the deep dive with Dustin on Blockbuster Please subscribe and leave a review.

In this episode of the SuperDataScience Podcast, I chat with Head of Data Jaco Van Der Berg. You will talk about many different topics in the space of data such as data strategy, data security, data flow within an organisation and the single point of truth. You will also get to learn about managing and hiring for a data science team and will be able to gain lots of knowledge all about the structure of a data team. If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/55

It seems like every company is trying to come up with an AI and machine learning strategy. Monica Rogati is an independent data science advisor, and she has some news: You can’t just lock a few data scientists in a room and expect them to sprinkle “magical machine learning dust” on everything. In this episode, she explains how a company can develop a successful data strategy, build a strong data team, and hire (and retain) talented data scientists.

Want to get some quick insights into how to build a data science team? Tap into the expertise of Chris Matys, the Chief Analytics Officer at Georgian Partners. He explains the four primary skill sets you need to look for in data scientists and how to create a data science team to help you meet your business needs.

Dean Eckles presents his research as part of Facebook’s Data Science Team. At Facebook, Eckles explores peer effects - how ideas, images and other elements spread in networks.

Dean Eckles presents his research as part of Facebook’s Data Science Team. At Facebook, Eckles explores peer effects - how ideas, images and other elements spread in networks.