High Signal: Data Science | Career | AI
Welcome to High Signal, where you’ll hear the best from the best in data science, machine learning, and AI. The goal of this podcast is to bring high signal, to help you advance your careers in data science, ML, and AI.
Displaying Episode 1 - 10 of 13 in total of High Signal: Data Science | Career | AI with the tag “machine learning”.
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Episode 18: High-Stakes AI Systems and the Cost of Getting It Wrong
June 19th, 2025 | 58 mins 45 secs
ai, data science, llms, machine learning
Sudarshan Seshadri—VP of AI, Data Science, and Foundations Engineering at Alto Pharmacy—joins us to explore what it takes to build high-stakes AI systems that people can actually trust. He shares lessons from deploying machine learning and LLMs in healthcare, where speed, safety, and uncertainty must be carefully balanced. We talk about designing AI to support pharmacist judgment, the shift from bottlenecks to decision backbones, and why great data leaders are really architects of how irreversible decisions get made.
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Episode 17: The Incentive Problem in Shipping AI Products — and How to Change It
May 29th, 2025 | 53 mins 52 secs
ai, data science, machine learning
Roberto Medri, VP of Data Science at Instagram, explains why most experiments fail, how misaligned incentives warp product development, and what it takes to drive real impact with data science. He shares what teams get wrong about launches, why ego gets in the way of learning, and how Instagram turned Reels from a struggling product into a global success. A candid look at product, data, and decision-making inside one of the world’s most influential platforms.
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Episode 16: How Human-Centered AI Actually Gets Built
May 13th, 2025 | 47 mins 22 secs
data science, genai, llms, machine learning
Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be understood as a civilizational technology—one that shapes individuals, communities, and society at large.
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Episode 15: Why Good Metrics Still Lead to Bad Decisions — and How to Fix It
April 24th, 2025 | 54 mins 17 secs
data science, genai, llms, machine learning
Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and co-designer of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization.
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Episode 14: Why Most Companies Aren’t Actually AI Ready (and What to Do About It)
April 9th, 2025 | 51 mins 58 secs
data science, genai, llms, machine learning
Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.
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Episode 13: The End of Programming As We Know It
March 27th, 2025 | 1 hr 23 mins
ai, data science, genai, llms, machine learning
Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.
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Episode 12: Your Machine Learning Solves The Wrong Problem
March 13th, 2025 | 54 mins 40 secs
causal inference, causal ml, data science, machine learning
Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
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Episode 10: AI Won't Save You But Data Intelligence Will
February 12th, 2025 | 59 mins 42 secs
ai, data science, machine learning
Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.
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Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson
January 30th, 2025 | 1 hr 9 mins
ai, data science, machine learning
Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—explains why most companies fail to fully leverage their data science teams. Drawing on his experience leading data functions at top tech companies, he shares how organizations can move beyond treating data science as a support function and instead empower data scientists to drive strategic impact through experimentation, iteration, and algorithmic decision-making.
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Episode 8: From Zero to Scale: Lessons from Airbnb and Beyond
January 9th, 2025 | 1 hr 6 mins
ai, data science, machine learning
Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadership, trust, and experimentation.