High Signal: Data Science | Career | AI

Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.

LINKS

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.

SHOW NOTES

In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as a service function to empowering them as strategic drivers of business impact.

Key topics from the conversation include:

💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

In this episode of High Signal, 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.

Key topics from the conversation include:

💡 Tune in to explore how data science principles can scale across industries, the leadership skills required to build impactful teams, and why experimentation is as relevant to ice cream as it is to AI systems.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact.

Key topics from the conversation include:

🎧 Tune in to learn how to build decision systems, integrate causality into workflows, and develop scalable data science teams for real-world impact.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the data landscape.

Highlights from the discussion include:

The conversation concludes with Hilary’s optimistic take on how the data science community can continue to thrive by embracing creativity, empathy, and interdisciplinary collaboration.

🎧 Tune in to gain practical insights into building robust AI systems, navigating career shifts, and leveraging generative AI for meaningful innovation.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.

Highlights from the discussion include:

The conversation concludes with a forward-looking exploration of opportunities in government, education, and healthcare, and Gabriel’s optimism about building ecosystems where startups and local talent thrive.

🎧 Tune in to learn from Gabriel’s thoughtful perspectives on navigating the complexities of building data-driven cultures, the global AI landscape, and how to leverage data for impactful change.

You can find more on our website: https://high-signal.delphina.ai/

Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.

Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the risk aversion trap, the importance of learning from negative results, and how generative AI is reshaping the experimentation landscape.

We also talk about common failure modes and the types of things you're probably doing wrong, along with strategies to avoid these pitfalls. Plus, we discussed the role of incentives, the necessity of data driven decision making, and what it means to experiment in high stakes environments.

Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.

Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.