Eric Colson, former Chief Algorithms Officer at Stitch Fix and VP of Data Science at Netflix, discusses why 90% of data science initiatives fail. He emphasizes the need to treat data scientists as strategic drivers rather than mere service providers. Colson highlights the power of cognitive repertoires in problem-solving and advocates for a culture of experimentation, where trial and error leads to innovation. He also shares insights on restructuring data teams to transform them from cost centers into revenue generators, enhancing business value through collaboration.

Elena Grewal, former Head of Data Science at Airbnb, political consultant, professor at Yale, and an ice cream shop owner, discusses her impressive career in building data teams. She shares how she scaled Airbnb’s data function and why trust is essential for effective teamwork. Elena explains applying data science in diverse fields, including politics and running an ice cream business. She emphasizes the importance of experimentation in decision-making and critical thinking for future leaders, illustrating that data principles are universal, from tech to ice cream.

Chris Wiggins, Chief Data Scientist at The New York Times and a Columbia University professor, discusses the transition from predictive to prescriptive analytics. He emphasizes the importance of actionable decision systems, highlighting how hospitals could benefit from prescription-based treatments. Wiggins introduces the AI Hierarchy of Needs, outlines strategies for scaling data teams, and underlines the necessity of empathy in data science for effective collaboration. His insights help bridge the gap between advanced technology and practical organizational applications.

Hilary Mason, a renowned data scientist and co-founder of Hidden Door, dives into the transformative landscape of data science amid the rise of AI. She emphasizes the crucial role of human judgment in guiding AI outputs and warns against over-reliance on prompts, advocating for rich contextual approaches. Highlighting her company's mission, Hilary discusses turning AI's challenges into creative storytelling opportunities. She also offers insights on navigating career paths in the evolving job market, stressing the need for empathy and critical skills in a world shaped by automation.

Gabriel Weintraub, the Amman Professor of Operations at Stanford, shares his wealth of experience from Uber and Mercado Libre. He discusses bridging the gap between leadership and tech teams to foster data-driven organizations. Gabriel emphasizes the importance of starting with foundational steps in AI adoption and creating a culture that celebrates experimentation. He also highlights the unique AI opportunities in Latin America and the transformative power of generative AI for smaller teams, advocating a problem-first approach to drive impact.

Ramesh Johari, a Professor at Stanford University, dives into the evolution of online experimentation, especially for tech companies and marketplaces. He discusses how organizations can shift to self-learning models and the common pitfalls they encounter, such as risk aversion. The conversation touches on the transformative impact of generative AI on experimentation processes. Ramesh also shares strategies for cultivating a culture of learning from failure and integrating data scientists to enhance business value, all while moving beyond traditional A/B testing methods.

Chiara Farronato, an Associate Professor at Harvard Business School specializing in digital platforms, shares insights on the transformation of sectors through companies like Airbnb and Uber. She highlights the critical need for effective communication between managers and data scientists to foster better collaboration. Chiara discusses the importance of bridging gaps in understanding, particularly in product management, and explores the challenges traditional industries face in adopting data-driven cultures. Her experiences offer valuable lessons for business leaders navigating platform-based innovation.

Hugo Bowne-Anderson chats with Andrew Gelman, a Columbia University professor specializing in statistics and political science. They delve into the necessity of high-quality data and the vital role of causal inference in decision-making. Andrew emphasizes the importance of simulations to avoid misleading conclusions, while also discussing the significance of a coder’s mindset in statistical analysis. The conversation wraps up with insights on voting's impact and the challenges of generalizing from sample data in polling, shedding light on the complexities of statistical interpretation.

Michael Jordan, a leading Professor at UC Berkeley, dives into the future of AI and its planetary-scale potential. He discusses the integration of machine learning, computer science, and economics to tackle complex challenges. The conversation highlights the issues of uncertainty in AI, the importance of collective intelligence in decision-making, and how reliable data can enhance predictive accuracy. Jordan emphasizes the need for responsible technology that positively impacts society, balancing innovation with the necessity for authentic human creativity.