Podcasts about data science lead

Entrepreneur Jeremy Shane joins host Ross Katz to discuss Life for Health, a revolutionary framework aiming to tackle chronic disease through the integration of life insurance, longitudinal data, and preventative care.  From drug discovery feedback loops to outcomes-based pricing, Shane outlines a bold new path for biotech and healthcare convergence. ​​What You'll Learn in This Episode: Why today's healthcare system fails chronic disease patients and how Life for Health aims to fix it.How longitudinal data and life insurance can realign healthcare incentives for better outcomes.The economic logic behind outcomes-based annuity pricing for breakthrough drugs like GLP-1s.What biotech companies gain from a proactive, participant-driven health model.How clinical trials could transform through pre-qualified data-rich populations. Meet Our Guest Jeremy Shane, Venture Partner at NextGen Venture Partners, is a healthcare innovator focused on chronic disease. With leadership experience at HealthCentral, WebMD, and 2U, he now pioneers Life for Health, blending life insurance with preventative care to realign incentives and extend health span. Life for Health, the book, will be released in July 2025.  To learn more about Life For Health or get involved, go to www.lifeforhealth.com About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Jeremy Shane on LinkedIn or on Twitter @jeremylshane Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

In this episode of Data in Biotech, host Ross Katz sits down with Scott Lipnick, Co-Founder and President of Etiome, to explore how preemptive medicine is changing the way biotech approaches therapeutic modeling and intervention.  Discover how Etiome's recently launched, AI-driven platform is built to detect disease earlier, identify precise biomarkers, and create stage-specific treatments—all before symptoms arise.  It's a visionary approach to healthcare focused on preserving health, not just managing illness. What You'll Learn in This Episode: Why Scott Lipnick believes the future of medicine lies in preemptive care.How Etiome uses AI to map disease progression at the cellular level.The role of biomarkers in identifying disease before symptoms surface.How Etiome applies its Temporal Biodynamics platform across diseases.What venture creation looks like at Flagship Pioneering. Meet Our Guest Scott Lipnick is the Co-founder and President of Etiome. At the forefront of preemptive medicine, Scott's work focuses on predicting disease progression and delivering personalized, early-stage interventions using cutting-edge AI and molecular tools. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyFind out more about EtiomeConnect with Scott on LinkedIn Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

Listen as Patrick Nessenthaler, Partner and Co-Founder of CREnetics, and John Gorgy, Data Science Lead at CREnetics, discuss how artificial intelligence (AI) decision-support tools are helping real estate executives make better, faster, and more forward-looking decisions.

How do you drug the undruggable? In this episode of Data in Biotech, Ross Katz sits down with Will Fondrie, Head of Data Science and Engineering at Talus Bio, to explore how machine learning, mass spectrometry, and innovative computational models are transforming drug discovery. Learn how Talus Bio is targeting transcription factors—once considered out of reach—with scalable, high-impact data science. What You'll Learn in This Episode Why transcription factors are historically hard to drug and how Talus Bio is changing thatHow mass spectrometry offers high-throughput, unbiased views of protein-DNA interactionsThe role of recommender systems in prioritizing compound testingStrategies for balancing build vs. buy in data infrastructure at scaleWhy open-source software is essential for scientific transparency and progress Links: Find out more about Talus Bio: https://talus.bioConnect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Connect with Will Fondrie on LinkedIn: https://www.linkedin.com/in/wfondrie/ Meet Our Guest Will Fondrie is the Head of Data Science and Engineering at Talus Bio, a biotech company pioneering the development of small molecule drugs targeting transcription factors. With a PhD in molecular medicine and a background in proteomics, Will brings deep expertise in computational biology, machine learning, and scalable data systems. About the Host Ross Katz is the Principal and Data Science Lead at CorrDyn. He hosts Data in Biotech to spotlight innovative thinkers and data-driven leaders pushing the boundaries of biotechnology. Enjoying the show? If you liked this episode, consider sharing it with a colleague and exploring more conversations at Data in Biotech. Your support helps us keep delivering expert insights on the future of biotech. Sponsored by CorrDyn This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.

How can AI improve clinical trials and accelerate drug development?  In this episode of Data in Biotech, Ross Katz sits down with Patrick Leung, CTO of Faro Health, to explore how AI-driven tools are reshaping clinical trial design. Patrick shares insights into document generation, patient burden analysis, and AI governance in biotech.  Learn how Faro Health is developing clinical protocols and leveraging AI to optimize trial success while ensuring regulatory compliance. Whether you're in biotech or healthcare, this conversation offers valuable takeaways on the future of AI in increasing clinical trial efficiency.  What You'll Learn in This Episode: How AI is used to generate clinical trial protocols and reduce inefficiencies.The role of AI in assessing patient burden and optimizing trial designs.How data model development enables specialized biomedical AI workflows.How large language models (LLMs) support clinical trial automation.Future trends in AI-driven clinical trial optimization. Links: Find out more about Faro Health: https://www.farohealth.comConnect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Connect with Patrick Leung on LinkedIn: https://www.linkedin.com/in/puiwah/ Meet Our Guest: Patrick Leung is the Chief Technology Officer at Faro Health, where he leads AI-driven innovations in clinical trial design. With a background in data science and software engineering from companies like Google and Two Sigma, Patrick brings a fresh perspective to life sciences, focusing on optimizing clinical trials through AI and structured data models. About the Host: Ross Katz is the Principal and Data Science Lead at CorrDyn, specializing in applying data science to biotech and healthcare. As the host of Data in Biotech, Ross explores the latest trends and innovations shaping the industry. Enjoying the Show? Visit Faro Health to learn more about AI-driven clinical trial optimization. Don't forget to rate and review Data in Biotech on Apple Podcasts! Sponsored by CorrDyn This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.

What's up everyone, today we have the pleasure of sitting down with Sundar Swaminathan, author of the experiMENTAL newsletter and part time Marketing and Data science advisor?Summary: After leading Uber's Marketing Data Science teams, Sundar shares insights that work for both tech giants and startups. Beyond uncovering that Meta ads generated zero incremental value (saving $30 million annually), they mastered measuring brand impact through geo testing and predicting LTV through first-week behaviors. Small companies can adapt these methods through strategic A/B testing and simplified attribution models, even with limited sample sizes. Building data science teams that embrace business impact over technical complexity, and maintaining curiosity, like when direct driver engagement revealed that recommending Saturday afternoon starts over Friday peak hours improved retention. About SundarSundar started his career as a software developer at Bloomberg before managing $19 Trillion at the US Treasury as a Debt ManagerHe pivoted to growth marketing and data science consulting where he worked with DirectTV and an ed-tech AI startupHe then made the mega move to Uber where he spent 5 years building Brand, Performance, and Lifecycle Marketing Data Science teamsHe moved over to a travel tech startup and helped them go from $0 to $100K MRRToday, Sundar is a marketing and data science advisor, he helps B2C founders and marketers He's also working on an upcoming podcast and has a newsletter where he shares frameworks, how-to guides to help B2C marketersMarketing Incrementality Testing Reveals Meta Ads Ineffective at UberPerformance marketing often reveals surprising truths about channel effectiveness, as demonstrated by a fascinating case study from Uber's marketing operations. When confronted with unstable customer acquisition costs (CAC) that fluctuated 10-20% week over week despite consistent ad spend on Meta platforms, Uber's performance marketing team, led by Sundar, decided to investigate the underlying causes.The investigation began when the team noticed significant volatility in signup rates despite maintaining steady advertising investments. This inconsistency prompted a deeper analysis of Meta's effectiveness as a primary performance marketing channel. The timing of this analysis was particularly relevant, as Uber had already achieved substantial market penetration eight years after its launch, especially in major urban markets where awareness wasn't the primary barrier to adoption.Through rigorous data analysis, the team implemented a three-month incrementality test to measure Meta's true impact on user acquisition. The test utilized a classic A/B testing methodology, comparing a control group receiving no paid ads against a treatment group exposed to Meta advertising. The results were striking: Meta advertising showed virtually no incremental value in driving new user acquisition, a finding that was validated by Meta's own data science team.The outcome of this experiment led to a significant strategic shift, resulting in annual savings of approximately $30 million in the U.S. market alone. While this figure might seem modest for a company of Uber's scale, its implications were far-reaching when considered across global markets. The success of this experiment also highlighted the importance of data-driven decision-making and the willingness to challenge assumptions about established marketing channels.Key takeaway: Established marketing channels should never be exempt from rigorous effectiveness testing. Regular incrementality testing can reveal unexpected insights about channel performance and lead to substantial cost savings. Marketing teams should prioritize data-driven decision-making over assumptions about channel effectiveness, even for seemingly essential platforms.How to Run Marketing Experiments With Limited DataMost companies don't have the volume of signups or users that an Uber does. Marketing experiments require a mindset shift when working with small data samples. While A/B testing remains the gold standard for measuring marketing effectiveness, Sunday thinks that companies with limited data can still validate their marketing efforts through strategic pre-post testing approaches.Pre-post testing, when properly implemented, serves as a valuable tool for measuring marketing impact. The key lies in isolation: controlling variables and measuring the impact of a single change. For instance, a marketplace company successfully conducted a pre-post test on branded search keywords in France by isolating specific terms in a defined region. This focused approach provided reliable insights despite not having the massive data volumes typically associated with incrementality testing.That being said, Sundar adds that early-stage companies should prioritize high-impact experiments capable of delivering substantial results vs testing tiny changes that will barely have detectable effects. With small sample sizes, tests should target minimum detectable effects (MDE) of 30-40%. These larger effect sizes become measurable even with limited data, making them ideal for fundamental changes such as exploring new ideal customer profiles (ICPs) or revamping core value propositions, rather than pursuing minor optimizations.An example that Sundar recalls while working at a travel tech startup demonstrated the value of running A/B tests even with limited data. Despite having only 100-200 weekly signups, they detected a 40% conversion drop after modifying their onboarding flow. While the test might have been considered "poorly powered" by strict statistical standards, it successfully prevented a significant negative impact on the business. This illustrates how even small-scale testing can provide crucial insights; it's better to have 60% confidence in a positive change than to miss a catastrophic drop with 95% confidence.The confidence level in marketing experiments operates on a spectrum, with A/B tests providing the highest confidence and pre-post tests offering valuable but less definitive insights. Success depends on maintaining experimental discipline, carefully controlling variables, and understanding the tradeoffs between confidence levels and the humbling reality of practical constraints. Marketing teams must balance their confidence requirements against their risk tolerance when designing and interpreting tests.Key takeaway: Companies with limited data should focus on measuring high-impact marketing changes through carefully controlled pre-post tests. Success comes from isolating variables, targeting substantial effect sizes, and maintaining experimental discipline. This approach enables meaningful measurement while acknowledging the practical constraints of smaller data sets.The Difference Between AB Testing and Incrementality TestingMarketing experimentation terminology often creates unnecessary complexity in what should be straightforward concepts. The fundamental structure of both A/B testing and incrementality testing follows the same principle: comparing outcomes between groups that receive different treatments.Statistical analysis remains consistent across both testing approaches. Whether using Bayesian or frequentist methods, the underlying comparison examines differences between groups, regardless of what those groups receive. The statistical calculations remain indifferent to whether one group receives no treatment (as in incrementality tests) or a variation of the treatment (as in traditional A/B tests).Incrementality testing extends beyond simple presence versus absence comparisons. For example, marketers can test spending increm...

In this episode, Jackie Clayton, Analytics & Data Science Lead at Mars, discusses creating effective learning cultures in data teams. Drawing from her experience establishing data academies, Jackie shares insights on:  Integrating learning into daily work  Making the most of your learning budget  Learning without having to spend money for it  Involving all team members in the learning process  Extending data education across organisations  She offers practical advice for fostering a learning environment in both large and small teams, emphasising the importance of aligning learning with business goals and sharing knowledge. Jackie also touches on how one can strategically use learning budgets when available. 

Jon Moore, Head of Consulting Services and Client Success and Chief Risk Officer, Clearwater, speaks with Leah Voigt, Chief Compliance Officer, Corewell Health, and Dr. Mark Sendak, Population Health and Data Science Lead, Duke Institute for Health Innovation, about the policies, procedures, and structures that guide the application of artificial intelligence (AI) in health care. They discuss developing ethical principles and decision-making processes to guide AI use cases, fostering effective collaboration and dialogue about the use of AI, transparency and consent, federal agency and state law developments, ensuring representative data in creating AI, and opportunities and risks. Leah and Mark spoke about this topic at AHLA's 2024 Complexities of AI in Health Care in Chicago, IL. Sponsored by Clearwater.To learn more about AHLA and the educational resources available to the health law community, visit americanhealthlaw.org.

Join host Waheed Najimi in this episode as he delves into the strategies and challenges of building and leading data science teams in the Nordic region. Featuring insights from industry experts Johanna Staaf, Director Data & AI at Capgemini Invent, Johnny Petersson, Product Owner and Data Steward at Volvo Cars, Sarah Berenji, Lead Machine Learning Architect at Telia, and Milosz Bolibrzuch, Data Science Lead at Twigeo. Discover actionable advice and best practices for navigating the dynamic landscape of data-driven innovation.

 Recorded on Sep 13, 2023 in Beit El'Azari, IsraelVideo version available on YouTubeThe eternal dance between the data and the modelEarly in his career, Iyar realized that purely associative models cannot provide him with the answers to the questions he found most interesting. This realization laid the groundwork for his search for methods that go beyond statistical summaries of the data. What started as a lonely journey, led him to become a data science lead at his current company, where he fosters causal culture daily. Iyar developed a framework that helps digital product companies make better decisions regarding their products at scale and at budget. Here, causality is not just a concept, but a tool for change. Ready to dive in?------------------------------------------------------------------------------------------------------ About The GuestIyar Lin is a Data Science Lead at Loops, where he helps customers make better decisions leveraging causal inference and machine learning methods. He holds master's degree in statistics from The Hebrew University of Jerusalem. Before Loops, he worked at ViaSat and SimilarWeb. Connect with Iyar: - Iyar on LinkedIn- Iyar's web page About The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4). Connect with Alex: - Alex on the InternetLinksPapers - Breiman (2001) - Statistical Modeling: The Two CulturesBooks - Molak (2023) - Causal Inference and Discovery in Python- Pearl et al. (2016) - Causal Inference in Statistics - A PrimerCausal Bandits TeamProject Coordinator: Taiba Malik (Insta)Video and Audio Editing: Navneet Sharma, Aleksander Molak #machinelearning #causalai #causalinference #causality Causal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

There's growing excitement that artificial intelligence can make health care better by speeding up care, improving diagnoses and easing the burden on a burned out workforce. But there are also concerns that these powerful new tools will perpetuate biases and inequities long baked into our health care system.In Part 2 of our special series on racial bias in health care AI, we dig into what the Biden administration is doing to keep biased algorithms from getting to the bedside.Guests:Emily Sterrett, MD, Associate Professor of Pediatrics, Director of Improvement Science, Duke University School of Medicine Department of PediatricsMark Sendak, MD, MPP, Population Health & Data Science Lead, Duke Institute for Health InnovationMinerva Tantoco, Chief AI Officer, New York University McSilver Institute for Poverty, Policy and ResearchCarmel Shachar, JD, MPH, Executive Director, Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law SchoolKathryn Marchesini, JD, Chief Privacy Officer, Office of the National Coordinator for Health Information TechnologyMelanie Fontes Rainer, JD, Director, HHS Office for Civil RightsLearn more and read a full transcript on our website.Dan Gorenstein will moderate three one-on-one discussions featuring industry leaders and top officials from ONC, FDA, and HHS' Office of Civil Rights over two plenary sessions, you can watch them here.Support this type of journalism today, with a gift, which for a limited time will be matched! Hosted on Acast. See acast.com/privacy for more information.

There's growing excitement that artificial intelligence can make health care better by speeding up care, improving diagnoses and easing the burden on a burned out workforce. But there are also concerns that these powerful new tools will perpetuate biases and inequities long baked into our health care system.In the first of two back-to-back episodes on racial bias in health care AI, we explore the challenge of diagnosing bias in AI and what one health system is trying to do about it.Guests:Emily Sterrett, MD, Associate Professor of Pediatrics, Director of Improvement Science, Duke University School of Medicine Department of PediatricsMark Sendak, MD, MPP, Population Health & Data Science Lead, Duke Institute for Health InnovationGanga Moorthy, MD, Global Health Fellow, Duke Pediatric Infectious Disease ProgramPaige Nong, PhD Candidate, University of Michigan School of Public HealthLearn more and read a full transcript on our website.Dan Gorenstein will moderate three one-on-one discussions featuring industry leaders and top officials from ONC, FDA, and HHS' Office of Civil Rights over two plenary sessions, you can watch them here.Support this type of journalism today, with a gift, which for a limited time will be matched!Want more Tradeoffs? Sign up for our free weekly newsletter featuring the latest health policy research and news. Hosted on Acast. See acast.com/privacy for more information.

מה שילוב של AI יכול לאפשר למחלקת ה-CX? איך מוודאים שהמענים שהמודל נותן עומדים בסטנדרט שלנו? ואיך בונים בוט כזה בפועל?  חודש אוגוסט הגיע ואנחנו עומדים להקדיש את כולו לנושא שיהיה לכם קל לצרוך גם אם אתם בחופש - AI. בכל פרק בחודש, נסקור איך אפשר להשתמש ב-AI כדי לאפטם ולשפר תחום אחר בסטארטאפ שלנו - מה הכלים שקיימים, ואיך נכון להשתמש בהם.  ובפרק הפעם אנחנו מדברים על איך אפשר להשתמש ב-AI כדי לייעל את כל תחום שירות הלקוחות. שירות לקוחות טוב משמעותי לכל סטארטאפ ועוזר לחזק נאמנות ושימור משתמשים, כמו גם לבלוט בשוק תחרותי. עם זאת, אחד האתגרים הכי גדולים שמגיעים עם צמיחה של סטארטאפ הוא העלייה בכמות הטיקטים, וההבנה שאי אפשר להצמיח את כמות אנשי ה-CX בצורה ליניארית, מה שלרוב מייצר עומס רב על המחלקה. ההתפתחות הטכנולוגיות של הבינה המלאכותית, מאפשר להתמודד עם האתגר הזה על ידי הטמעה של AI במענה ללקוחות, אבל זה פתרון שיכול להוביל ללא מעט אתגרים חדשים - איך מוודאים שהמענה שניתן על ידי הבוט עומד בסטנדרט שלנו, האם ההתפתחות הזו מסכנת את המשרות של אנשי ה-CX הקיימים, ואיך עושים את המעבר הזה בצורה הדרגתית. השבוע אדוה שיסגל מדברת עם אוהד הגדיש, Data Science Lead במאנדיי, וזיו שכטמן, Systems Project Manager ב-CX, שהובילו את המהלך במאנדיי. אוהד וזיו משתפים בתהליך שעשו כדי להגיע למצב ש-6% מהטיקטים שלנו היום נענים על ידי בינה מלאכותית, ומה נדרש ועוד מאתגר בלהגדיל את האחוז הזה ולעשות למהלך הזה סקייל.  --- מוזמנים להצטרף אל קבוצת הפייסבוק שלנו ולהמשיך את השיח - www.facebook.com/groups/startupforstartup/ ניתן למצוא את כל הפרקים ותכנים נוספים באתר שלנו -   https://www.startupforstartup.com/See omnystudio.com/listener for privacy information.

Today, AI is the hot topic of discussion amongst the professionals of almost every field. There are debates going on if AI is going to replace human beings while some are emphasizing on the personal touch that only humans can bring in. there are aslo discussions on the misuse of AI. in today's discussion, with a leading data science expert, we discuss the impact of AI in the field and the caution that needs to be exercised while depending on it.  [00:37] - About Dr. Fatemeh Sharifi Dr. Fateman is a Data Science Leader at Avanade. She is an expert at software engineering, machine learning, artificial intelligence and data science.  --- Support this podcast: https://podcasters.spotify.com/pod/show/tbcy/support

Get ready to unlock the power of data literacy in kids with our guest, Gulrez Khan, Data Science Lead at PayPal & Author of the book 'Drawing Data with Kids.' In this episode, we explore the importance of nurturing data literacy skills in children from an early age. Discover the innovative strategies, activities, and insights shared by our expert guest to make data literacy engaging and accessible to young learners. Whether you're a parent seeking to empower your child or an educator eager to revolutionize your teaching, this conversation will inspire you with practical ideas and success stories. Join us as we dive into the world of data literacy and learn how it cultivates critical thinking, creativity, and problem-solving abilities in our little ones. 

How can you navigate a non-linear career path in the tech industry? In this podcast, Kaboo Founder Neha Shah meets with Atlassian Data Science Lead Nisha Lyer to discuss non-linear career paths, the power of continuous learning, and the significance of product-market fit. Gain valuable insights on leveraging user interviews, synthesizing data, and understanding customer needs.

There's growing excitement that artificial intelligence can make health care better by speeding up care, improving diagnoses and easing the burden on a burned out workforce. But there are also concerns that these powerful new tools will perpetuate biases and inequities long baked into our health care system.In Part 2 of our special series on racial bias in health care AI, we dig into what the Biden administration is doing to keep biased algorithms from getting to the bedside.Guests:Emily Sterrett, MD, Associate Professor of Pediatrics, Director of Improvement Science, Duke University School of Medicine Department of PediatricsMark Sendak, MD, MPP, Population Health & Data Science Lead, Duke Institute for Health InnovationMinerva Tantoco, Chief AI Officer, New York University McSilver Institute for Poverty, Policy and ResearchCarmel Shachar, JD, MPH, Executive Director, Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law SchoolKathryn Marchesini, JD, Chief Privacy Officer, Office of the National Coordinator for Health Information TechnologyMelanie Fontes Rainer, JD, Director, HHS Office for Civil RightsLearn more and read a full transcript on our website.Want more Tradeoffs? Sign up for our free weekly newsletter featuring the latest health policy research and news.Support this type of journalism today, with a gift.Follow us on Twitter. Hosted on Acast. See acast.com/privacy for more information.

There's growing excitement that artificial intelligence can make health care better by speeding up care, improving diagnoses and easing the burden on a burned out workforce. But there are also concerns that these powerful new tools will perpetuate biases and inequities long baked into our health care system.In the first of two back-to-back episodes on racial bias in health care AI, we explore the challenge of diagnosing bias in AI and what one health system is trying to do about it.Guests:Emily Sterrett, MD, Associate Professor of Pediatrics, Director of Improvement Science, Duke University School of Medicine Department of PediatricsMark Sendak, MD, MPP, Population Health & Data Science Lead, Duke Institute for Health InnovationGanga Moorthy, MD, Global Health Fellow, Duke Pediatric Infectious Disease ProgramPaige Nong, PhD Candidate, University of Michigan School of Public HealthLearn more and read a full transcript on our website.Want more Tradeoffs? Sign up for our free weekly newsletter featuring the latest health policy research and news.Support this type of journalism today, with a gift. Hosted on Acast. See acast.com/privacy for more information.

What does the future hold for batteries and the wider energy industry? Who knows! We say goodbye to last year and look ahead as Quentin and Alex discuss their predictions for the future in the first episode of 2023. Over the course of this episode, they discuss:A potential swing in the production of cell chemistries.What might the Balancing Mechanism look like for batteries in the year ahead?How the energy transition might be impacted by inflation and higher interest rates.Are we going to see gigantic batteries in 2023?Plus much more (and maybe a sneaky bonus point!)Referenced in this episode - Balancing Mechanism and National Grid ESO - four battery need-to-knows.About ModoModo is the all-in-one Asset Success Platform for battery energy storage. It combines in-depth data curation and analysis, asset revenue benchmarking, and unique research reports - to ensure that owners and operators of battery energy storage can make the most out of their assets. Modo's paid plans serve more than 80% of battery storage owners and operators in Great Britain.To keep up with all of our latest updates, research, analysis, videos, podcasts, data visualizations, live events, and more, follow us on Linkedin.If you want to peek behind the curtain for a glimpse of our day-to-day life in the Modo office(s), check us out on Instagram.

Anjali Menon, Solutions Consultant at AsiaPac and Leadership Fellow at Women Who Code , Runjhun Ratawal, Senior Lead at Women Who Code, and Sneha Saxena, Data Scientist at Grubhub and Data Science Lead at Women Who Code, share,”Statistics For Machine Learning.” They discuss descriptive and inferential statistics, when to use mean, median, or mode when analyzing data, and explain how to get the best results when sampling a population.

What's happening on the ground at this year's Code for America Summit? We wanted to find out, so we wandered around with a mic and asked people two questions: What does the theme of the summit: “Building a Path Forward Together” mean to them? What has been one of the highlights of the summit? Get a window into the best parts of the summit from five people who were there: Michelle Partogi, Service Designer at Code for America Linkedin: linkedin.com/in/michellepartogi/ Matthew Adendroff, Data Science Lead for Open Cities Lab Twitter: twitter.com/opencitieslabza Heather Benninghoven, Strategy Analyst for Benefits Data Trust Twitter: twitter.com/HBenninghoven and twitter.com/BeneDataTrust Hallie Martenson, Development Manager for Benefits Data Trust Twitter: twitter.com/HallieMartenson Felix Gilbert, President of Xcell Twitter: twitter.com/felixgilbert Code for America's Twitter account twitter.com/codeforamerica Richard Pietro's Twitter account twitter.com/richardpietro Derek Alton's Twitter account twitter.com/DerekAlton ReOpenGov Twitter account twitter.com/re_open_gov ABOUT Stories from the Open Gov is a podcast published by www.reopengov.org and is dedicated to telling the stories about what Open Government & Open Data look like. Your hosts are Richard Pietro and Derek Alton, Open Government & Open Data practitioners for the past 10 years. Listen and learn how Open Government & Open Data are becoming a reality! MUSIC ATTRIBUTION - Introduction & conclusion Singing Sadie - I Can't Dance freemusicarchive.org/music/Singing_…3_I_Cant_Dance Attribution-NonCommercial-ShareAlike 3.0 United States (CC BY-NC-SA 3.0 US) creativecommons.org/licenses/by-nc-sa/3.0/us/

Host- Udayan Guha Site Reliability Engineering Managerr- Rackspace Technology Udayan has a proven track record in delivering digital analytics solutions to configure fundamentally sound frameworks. He can precisely isolate business needs and develop valuable solutions to drive accuracy and ROI efficiency. He is a result-oriented leader with almost 17 years of industry experience who thrives in a fast paced and competitive environment. Speaker- Siddhant Mohapatra Data Scientist III- ZEE5 Siddhant is a part of one of the fastest growing start-up as a Data Science Lead which is based out of Bangalore. As of now, he has got 7 years of Data Science experience in various domains such as sales, retail and Customer Experience firms, with expertise in Data Management, Quantitative Modeling, Business Research and Data Analytics. He has worked in very diversified environments like growing start ups to MNCs like eBay and Flipkart .

In this episode of The AdPod Wayne chats with Ali Manning who is Co-Founder & COO at Chalice and Tylynn Pettrey who is Data Science Lead at Chalice.In this episode they cover what an algorithm is, how they can be applied in media buying, what makes an algorithm custom and will they make programmatic traders redundant.A truly brilliant episode that is packed full of insight, stories and thought leadership. See acast.com/privacy for privacy and opt-out information.

WWCode Conversations: Sneha Saxena, Data Scientist at Grubhub and Data Science Lead at Women Who Code, interviews Tish Looper, VP of Customer Success at TigerGraph. They discuss ways to ensure diversity in hiring, her work with customers and the importance of relationship building, her thoughts on mentorship, and how she found success in leadership. Episode Page Here. WWCode Career Nav: In celebration of Geologists Day on April 3, we're featuring Natasha Hendrick, Development Geoscience Manager and Principal Geophysicist at Santos Ltd. She'll be discussing her career journey and the path she took to geophysics, as well as details about her work and profession. Episode Page Here. WWCode Talks Tech: We're highlighting our Cloud Technical Track with a talk by Dana Black, Senior Product Design and Strategy Consultant at Greyshore, who will be discussing ways to build design systems while keeping accessibility in mind.

This episode features an interview with KJ Gupte, Data Science Lead at Tradeshift, a cloud-based platform for supply chain payments. KJ has had nearly 15 years of experience in the industry. Before Tradeshift, KJ served as Data Analytics Manager at PwC where she led a team of engineers and developers in making data-centric products for customers like Apple, Google, and HP. She is also a graduate of the Harvard Business Analytics Program, and says her experience in the Data Science Pipeline and Critical Thinking course felt like rewiring her brain to think about data in new ways. In this episode, KJ discusses translating technical data for her stakeholders, building credibility in data among the C-suite, and being a trailblazer in the supply chain economy.Quotes*“I think what has helped me drive success is being extremely collaborative with the team and extremely transparent. Because many times when you are transparent and you exchange your thoughts, that's where you get the most gains. So I might be thinking about data from an angle and the users might be thinking about it from a very different angle. And a lot of exchange needs to happen. Because not everybody looks at the data in the same way. Everybody comes with a very different skill set, whether you are an engineer, whether you are a data scientist, whether you are a CEO, CFO, everybody has a core skillset that they bring to the table. It's not always the same or uniform. So collaboration and transparency is always the key.”*“We are absolutely data-driven right now. I'm very glad I was hired at the point that I was, because that's when we were scaling up. And that's when we were sitting on like chunks of data that was so potent that it was just a matter of using it. And in fact, we are sitting on buyer and seller supply chain data, which is the core of the supply chain disruptions that are going on. So we are in so many ways trail blazers to the whole supply chain economy.”*“The leadership knew that there was a lot of work that needed to be done in data. So my core job was to actually convey that message. Initially I spent a lot of time giving presentations on just what we had, not going into complexities. But, um, presentations around, okay, this is what we have. This is the story that data is telling. And just making people curious, you know, making people interested about data. Because if, if your data is not telling a story, it is just numbers.”*”When it comes to data, things are needed as of yesterday. And the moment you get data, it is stale. You have data right now, but it has gone light years ahead. So you have to be extremely fast and evolve with the data. So that was definitely a challenge, but what helped me is I had a lot of visibility. Right from the time I was hired, I was directly working with leadership. I was working with engineering to see the lay of the land, I was working with my CEO, my CFO, I was working with the head of engineering to see what they were talking about, to understand the language.” Time Stamps[8:34] Driving success through collaboration and transparency[14:28] Tradeshift's journey to becoming data-driven[19:05] Getting visibility on the freshest data[24:08] How to build credibility and trust in the data from company executives[29:24] How to deriving more value for customers[34:47] Under Pressure: How KJ Gupte makes difficult decisions[38:39] Advice to budding data scientistsLinksConnect with KJ on LinkedInCheck out TradeshiftConnect with Rob on LinkedInFollow Rob on TwitterThanks to our friendsTruth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

Siddhant is a part of one of the fastest growing start-up as a Data Science Lead which is based out of Bangalore. As of now, he has got seven years of Data Science experience in various domains such as sales, retail and Customer Experience firms, with expertise in Data Management, Quantitative Modeling, Business Research and Data Analytics. He has worked in very diversified environments like growing start ups to MNCs like eBay and Flipkart .

According to Statista, in 2020, about 25% of GAFAM's employees are female, and only 20% of the leadership positions are female. Facing such a gender gap in tech and leadership, we are shifting our lens to the Allwomen Campus, where the reshaping of the next generation of female tech leaders is taking place. Laura Fernández, CEO and co-found and Idoia Martí , the Data Science Lead of AllWomen are joining us today to introduce what they are pioneering with AllWomen, how the campus dynamic is like, and the paths women can take to start and excel the data career.

Steve Lawton, Analytics and Data Science Lead, discusses the three choices Oracle has to offer around machine learning and data science. Learn more    

Interview with Tomeka Hill-Thomas – Tomeka is Data Science Lead of Advanced People Analytics at EY. She is one of the most recognized data scientists in the world. Tomeka is passionate, not only about her work, but also about what we can achieve with data.

Heute Nachmittag geht es raus in die Natur - zumindest thematisch: Es geht um “Feld, Wald und Wiesen”, genauer die Landwirtschaft und die Forstwirtschaft und damit zwei gigantische und essentielle Märkte. Im ersten Gespräch führt uns David Dohmen, Co-Founder und Data Science Lead von Ocell, durch die Digitalisierung der Forstwirtschaft. Ocell arbeitet an einem Analyseverfahren, das Fernerkundung mit künstliche Intelligenz verbindet und mit seiner „Dynamic Forest”-App Daten zugänglich und organisiert aufbereitet. Das Unternehmen hat gerade seine Seed-Finanzierung im sechsstelligen Bereich erfolgreich abgeschlossen. Im zweiten Gespräch freuen wir uns auf Jonathan Bernwieser, CEO & Co-Founder bei Agrando. Hier haben Yabeo Impact, Sony Innovation Fund und Investbridge sowie die Altinvestoren June Fund und das Londoner Family Office JLR Star gerade 12 Millionen Euro investiert. Das Münchner Startup bietet “eine Online-Lösung für Landwirte, Landhändler und Hersteller zum Ein- bzw. Verkauf und der Vermarktung landwirtschaftlicher Betriebsmittel” an. Agrando versteht sich dabei als Unterstützer bei der Beschaffsanalyse und der Prozessoptimierung.

Tune in to the first episode of our Tech Series hosted by Tom Grey, our Chief Technology Officer, where we focus on the technology we develop at Launchpad and our Portfolio Companies. In this episode Tom talks to Çağrı Cerrahoğlu, Technical Product Owner at LYTT, and Alessandro Delfino, Data Science Lead at LYTT, about combining domain and technology expertise to meet business goals instead of only leading with one or the other. Learn from the team about fibre optics applications from security to Oil & Gas to environmental monitoring and mining, and even detecting earthquakes.

The last thing we all need this year is an identity crisis. Fear not, hosts Nic Fillingham and Natalia Godyla are here with Maria Puertas Calvo, Data Science Lead of Microsoft's Identity Security and Protection Team, to learn how AI is being used to protect our personal identities. Maria also reveals previously undisclosed information – her favorite food and her famous top-secret recipe, so get ready to take notes!  Later, the hosts bring back a previous guest, Geoff McDonald, ML Research Lead at Microsoft to unpack his career in cybersecurity and how game hacking led him to where he is now.  In This Episode, You Will Learn: How offline detections are used for account compromise prevention  The importance of multi-factor authentication  How Microsoft is taking a new approach with AI to identify threats with real-time prevention   The problem with adversaries and malware attackers  Some Questions We Ask:  How is Microsoft applying AI to solve problems for account compromise prevention?  How do humans play a role in labeling data sets?  How is Microsoft measuring success of their new enhanced AI?  What is the future for neural networks?  Resources Maria's Blog Microsoft Security Blog Maria's LinkedIn Geoff's LinkedIn Nic's LinkedIn Natalia's LinkedIn Related: Listen to: Afternoon Cyber Tea with Ann Johnson Listen to: Security Unlocked: CISO Series with Bret Arsenault  Security Unlocked is produced by Microsoft and distributed as part of The CyberWire Network. 

Today we're sitting down with a roundtable of data science and machine learning experts from Spotify, PwC, and Google Cloud. What does it truly mean to be steeped in the data science industry and what considerations should be addressed as a practitioner?Roundtable Interviewees: Sanjay Agravat, Machine Learning Specialist at GoogleAlex Simonoff, Senior Data Scientist at SpotifyAbdallah MJ Musmar, Data Science Lead at PwC Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

How can AI's power be harnessed by retailers to drive sales and improve business performance? AI was the major buzzword of the past decade. Practically becoming a catch all term for robotics, gaming mechanics, machine learning and more. But how are some of the world's biggest retailers like H&M, Zara and Amazon using AI to their advantage? We're joined by our very own Data Science Lead, Alejandro Giacometti, to demystify what AI is, including how machines are helping retailers operate smarter and more efficiently. And no, robots are not going to take away our jobs. Sign up to our weekly Insider Briefing to get the latest industry news and exclusive market analysis here. It would make our day if you could rate, review, or subscribe to us! You can get in touch at unedited@edited.com or tweet us at @EDITED_HQ

Setelah beberapa minggu rehat, akhirnya gw bisa release episode bareng salah satu Data Science Lead di Traveloka data, Philip Thomas. Kali ini kita diskusi tentang stategi penerapan machine learning agar machine learning bisa ngasih impact yang optimal ke produk dan sebuah perusahaan atau organisasi. Ternyata kuncinya bukan hanya di algoritma training machine learning kalian! Tapi ada beberapa hal yang jauh lebih penting! Yuk simak buat tau itu apa! Buat kalian yang tertarik dengan oportunity Data Engineer dan Data Engineer di Fraud AI Platform-nya Traveloka. Yuk check dua link ini! (1) http://bit.ly/traveloka-ngobrol-data-engineer-id (2) http://bit.ly/traveloka-ngobrol-ai-platform-id --- Send in a voice message: https://anchor.fm/ngobrolinstartup/message Support this podcast: https://anchor.fm/ngobrolinstartup/support

In this interview we welcome Martina Pugliese, Data Science Lead at the "Tinder for clothes" app company Mallzee.