Fifteen years building AI teams and translating the work for the people who lead them.

I've spent fifteen years at the place where AI expertise meets business consequence — building data science teams, scaling products, and translating what's technically possible into what's strategically sound for the leaders making the decisions.

The work

I led marketing data science at Asana, grew data science at Numerator from four people to twenty-eight, and navigated AI in regulated healthcare environments at Olive AI. I've also held roles at Postmates, GuideStar, and most recently Meta, where I led data science for supply chain products serving AI and data center infrastructure.

Earlier, I was at Postmates and GuideStar. Before that, I built the Executive Programs division at Metis from scratch — designing the courses that taught non-technical C-suite leaders how to think about machine learning before it became a board-level conversation. That turned out to be the foundation for everything I do outside of my day job: advising, writing, and speaking for the leaders who need to understand AI without becoming data scientists.

The thinking

My PhD dissertation at Cornell examined data quality — specifically, how poor data practices undermine AI systems that look correct but fail at scale. That lens applies whether I'm thinking about Meta-scale supply chain or helping a leadership team evaluate their first AI initiative: the question isn't usually whether the model works, it's whether the system around the model is built to support it.

I write and speak on AI strategy, data quality, and what it actually takes to make AI work in organizations. I've spoken at ODSC, Accelerate AI, and the Gen AI X Summit. I'm currently writing a practical guide to AI and ML for business leaders.

The approach

Direct, practical, no theater. My assessments are honest because comfortable ones are expensive. I work best with leaders who want to know what's actually true about their AI situation — and what to do about it.