Data Science Leaders

Justin Johnson sat down one afternoon to build a new capability for ARIA, the AI research agent he runs on a supercomputer that sits on his desk. Before writing a single line, he checked what was already there and found that his own system had designed and built the exact thing he was about to start. Three months earlier. While he was asleep.
That was the moment Justin went, in his words, from first mover to second mover on his own project, and he's still working out what that means.
Justin brings:
How the “second mover” gap is widening
Where most senior data science leaders really fall on the copilot-versus-harness divide
How the old trade-off between mission and frontier technology is collapsing in biopharma

Be sure to check out Justin’s book, Builder-Leader: The AI Exoskeleton That Crosses the Gap, and his blog, Run Data Run.

Dave Johnson’s first pharma job wasn't in a lab. It was in a room, dark and cramped, stacked floor to ceiling with 30 years of decaying discovery chemistry data on every storage medium imaginable.
They called it “the cave.”
A physicist turned data leader, Dave has spent two decades solving the same problem: scientific data is generated for one purpose, poorly captured, and left abandoned.
Now co-founding Dash Bio after years leading data science at Moderna, Dave joins Thomas to make a case the industry would rather not hear: AI pilots keep failing because organizations can't understand their own data or tell whether anything they build improves how they work.
Listen in to hear:
Why "the cave" is still an accurate description of how most pharma companies manage data
Why clinical trial numbers mean nothing without the metadata behind them
How the US needs to treat China’s clinical trial volume as a wake-up call

When Cindy Tu first stepped onto a conference stage, it wasn’t part of a long-term plan. It was a turning point. A single speaking invitation shifted her role from quietly reviewing AI systems to actively shaping how governance is practiced across financial services. With a background spanning IT, data, and audit, Cindy brought a rare systems-level view to the table.
Now a rising voice in enterprise AI risk, she’s influencing how institutions think about oversight, how governance frameworks evolve, and why people are at the heart of successful implementation. Her perspective is informed not only by technical expertise, but by lived experience.
Cindy brings:
How to frame governance as an enabler and not a gatekeeper
Why third-party risk keeps rising as gen AI adoption accelerates
How to flip governance from the “no” guys to the “yes” guys

When Shub Agarwal joined an early conversational AI startup, he was building products in uncharted territory with emerging technology few had heard of. By late 2019, Google and Meta were aggressively recruiting him. But a sudden personal loss made him rethink his priorities, leadership, and the impact he wanted to create.
From fast-growth startups to a financial regulator, he's codified a nine-step AI product framework, teaches at USC, and developed practical approaches to trust, governance, and AI agents in organizations. His story reflects intentional leadership in a rapidly evolving field.
You’ll hear:
How leaders should think about AI agents as part of their teams and workflows
Why stepping back at key moments makes you a better leader
How to evaluate AI systems beyond performance metrics

Daniel Kraft, physician-scientist and founder of NextMed Health, joins Domino’s Chris McSpiritt to discuss how AI, data convergence, and systems thinking are accelerating healthcare’s shift from reactive treatment to proactive, personalized care. With a background spanning regenerative therapies, digital health, and aerospace medicine, Daniel offers a wide-lens view of the challenges and opportunities shaping the next era of clinical innovation. From the limitations of “sick care” to the promise of personalized poly-pills and AI-powered digital twins, this conversation explores how to unlock proactive, personalized, and accessible healthcare at scale.
Join us as we discuss:
How exponential tech is enabling predictive, preventative care and what’s slowing it down
Why “data-to-action” is the next frontier for AI in life sciences
What data scientists and tech teams can do now to drive clinical impact

Get more of Daniel’s insights at DanielKraftMD.net.