High Signal: Data Science | Career | AI

Chris Child, VP of Product at Snowflake and author of an MIT Technology Review report, dives into the paradox of AI increasing data teams' workloads. He discusses how data engineering is shifting from backend functions to vital business strategy. Chris highlights the need for data engineers to think like product managers and the challenges faced with LLMs lacking business context. He emphasizes the importance of investing in foundational governance and exploratory experimentation to navigate rapid AI changes effectively.

Liz Costa, Chief of Innovation and Partnerships at the Behavioural Insights Team, shares insights on AI adoption through the lens of behavioral science. She discusses the crucial role of understanding human behavior in fully utilizing AI's potential. Costa introduces a triad of motivation, capability, and trust as barriers to adoption, while emphasizing the importance of reframing AI deployment to alleviate skepticism. She also highlights the need for organizational experimentation to uncover valuable use cases, ensuring deep integration rather than mere automation.

Lance Martin, a machine learning engineer at LangChain, dives into the evolving landscape of AI engineering. He emphasizes the importance of context engineering and how traditional ML rules are becoming obsolete. The conversation covers why adaptable systems thrive, the architectural advantages of 'agent harnesses,' and the shift towards in-app user feedback for evaluating AI systems. Lance also shares insights on balancing autonomy in agents with human oversight and techniques for managing costs and performance in complex AI tasks.

Paras Doshi, Head of Data at Opendoor and former Amazon data leader, shares insights on building effective data functions. He discusses transforming fragmented analytics into a centralized asset, emphasizing the importance of having a 'seat at the table' for data teams. Paras argues that AI is set to create the '100x individual contributor,' enabling significant productivity boosts. He also covers the balance between batch and real-time ML, the role of data in strategic business decisions, and the essentials for a successful data career.

Vishnu Ram Venkataraman, a Generative AI executive with a history at Credit Karma and Intuit, explores the hidden costs of generative AI. He discusses the disparity between quickly shipped prototypes and the long-term challenges of non-deterministic systems. Vishnu highlights the declining shelf value of generated code, advocating for an iterative focus. He stresses the importance of managing sensitive data and utilizing synthetic data for development. Vishnu also suggests a new organizational triad for effective team collaboration in the evolving AI landscape.

Sergey Fogelson (VP of Data Science, Televisa Univision) joins High Signal to reveal how the world’s largest Spanish-language media company built a sophisticated data engine from the ground up. This transformation fueled a tenfold expansion of its digital streaming business by redefining how the company connects with 300 million viewers worldwide. At the heart of this success is a proprietary household graph that creates a single, privacy-first view of a massive and culturally diverse audience. We dig into the journey from basic data unification to building production-ready recommendation engines, how his team uses embeddings on user behavior to uncover surprising connections in content consumption, and the trade-offs between investing in internal data tools versus direct revenue-driving products. The conversation also explores a pragmatic framework for AI adoption, showing how foundational machine learning often outperforms chasing the latest trends and where LLMs can deliver real, measurable value. LINKS Sergey Fogelson on LinkedIn Watch the conversation on YouTube Delphina's Newsletter

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