High Signal: Data Science | Career | AI

Andrés Bucchi (Chief Data Officer, LATAM Airlines) joins High Signal to unpack how a century-old airline reinvented itself with data and AI—and how that transformation is unlocking value from fuel efficiency to fraud detection. LATAM has built a massive data operation, experimenting across everything from pricing to operations, while customers benefit from a more reliable and secure travel experience. We dig into how LATAM fostered an experimentation culture, why existing data infrastructure is a critical asset, and how the biggest bottleneck in AI adoption isn't the technology itself, but human decision-making. The conversation also looks ahead to the future of generative AI as a software engineering problem, and the organizational changes needed to unlock its full potential. LINKS Andrés Bucchi on LinkedIn Tim O'Reilly on The End of Programming As We Know It, High Signal Watch the conversation on YouTube Delphina's Newsletter

Anu Bharadwaj, the President of Atlassian, shares insights on how AI agents are transforming teamwork across industries like publishing and finance. She discusses the success of bottom-up experimentation in creating effective AI tools and how non-technical teams are leading the charge. The conversation covers the importance of reliability in AI outputs, the future of proactive workflows, and the challenges of user interaction as AI evolves from tools to teammates. Anu also explores the exciting possibilities of multiplayer interactions between humans and AI.

Tomasz Tunguz, an investor at Theory Ventures specializing in SaaS and AI, dives into how AI is reshaping enterprise software and creating a $1 trillion market opportunity. He discusses the rapid evolution of workflows outpacing traditional software, the concept of 'liquid software' in CRM, and the need for a new type of 'agent inbox.' The conversation also touches on hidden AI technical debt, the importance of modular stacks over monolithic apps, and the challenges of scaling AI with current memory architectures.

Amy Edmondson (Harvard Business School) and Mike Luca (Johns Hopkins) join High Signal to unpack what actually drives good decisions in data‑rich organizations. Using contrasts like the Bay of Pigs vs. the Cuban Missile Crisis and product cases such as Airbnb’s work on measuring discrimination, they show how decision quality tracks conversation quality—framing options, surfacing uncertainty, and challenging assumptions. We cover common failure modes (correlation vs. causation, anchoring, hierarchy, false precision), practical meeting designs that raise the signal, and where algorithms and LLMs help or hinder human judgment. LINKS Amy on LinkedIn Mike on LinkedIn Where Data-Driven Decision-Making Can Go Wrong: Five pitfalls to avoid by Michael Luca and Amy C. Edmondson Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results Trillion Dollar Coach by Eric Schmidt, Jonathan Rosenberg, and Alan Eagle Delphina's Newsletter

Daragh Sibley, Chief Algorithms Officer at Literati and former Director of Data Science at Stitch Fix, joins High Signal to unpack how machine-learning moves from slide-deck promise to bottom-line impact. He walks through his shift from academic research on how kids learn to read to owning inventory and personalization algorithms that decide which five books land in every child’s box. We dig into the moment a data leader stops advising and starts owning P&L-critical calls, why some problems deserve simple analytics while others need high-dimensional models, and how to design workflows where human judgment and algorithmic predictions share accountability. Along the way we talk incentive design, balancing exploration and exploitation in inventory, and measuring success in dollars—not dashboards. LINKS Daragh on LinkedIn Eric Colson on Why 90% of Data Science Fails—And How to Fix It Sudarshan Seshadri on High-Stakes AI Systems and the Cost of Getting It Wrong Delphina's Newsletter

Lis Costa, Chief of Innovation and Partnerships at the Behavioural Insights Team, joins High Signal to explore how behavioral science is reshaping public policy, digital platforms, and machine learning. She explains how defaults influence behavior at scale, why personalization and chatbots are unlocking new kinds of interventions, and what happens when AI systems meet real-world complexity. We also discuss the limits of nudging, the promise of boosting, and why building for human decision-making requires more than just good models. LINKS The Behavioral Insights Team Lis Costa on LinkedIn High Signal podcast Delphina's Newsletter

Sudarshan Seshadri, VP of AI, Data Science, and Foundations Engineering at Alto Pharmacy, dives into the complexities of high-stakes AI in healthcare. He discusses the balance of speed, safety, and uncertainty that influences AI design and its support for pharmacists. The conversation highlights the evolution of data in decision-making, the rise of full-stack pharmacy practitioners using AI, and the essential role of data leaders as architects of irreversible decisions in ensuring patient care and operational efficiency.

Roberto Medri, VP of Data Science at Instagram, discusses the pitfalls of misaligned incentives in product development. He candidly reveals why most experiments fail and how ego can hinder learning. Medri shares insights on transforming Instagram Reels from a struggling feature to a global hit, emphasizing the balance between data-driven strategies and bold leadership. He highlights the importance of experimentation and rapid learning, and explains how embracing humility can drive meaningful impact in tech.

Fei-Fei Li, co-director of Stanford’s Human-Centered AI Institute, dives into the evolution of artificial intelligence, reflecting on her personal journey and the pivotal role of curiosity in shaping innovation. She champions the need for human-centered design in AI, discussing its transformative potential in healthcare, education, and sustainability. Li also explores the concept of spatial intelligence and urges a collaborative approach to build a sustainable AI ecosystem, emphasizing the importance of dignity and community empowerment in technology.

Eoin Mahony, data science partner at Lightspeed and former Uber science lead, shares his insights on effective metrics. He argues that metrics can mislead if their underlying mechanisms aren’t understood, a lesson he learned while optimizing NYC's Citi Bikes. Eoin discusses the pitfalls of relying too heavily on simplistic data and emphasizes the need for rigorous analysis in data-driven decision-making. He also dives into how generative AI can improve workflows while navigating the hype surrounding tech adoption, blending practical advice with real-world examples.