I help C-suite leaders build the latter — translating AI complexity into business clarity, and designing products that generate real returns across their full lifecycle.
About
I've led data science and AI product across industries and company stages — most recently at Meta, and as Sr Director of AI Product at Charles Schwab — and built the Executive Programs division at Metis that taught C-suite leaders how to think about machine learning before it became a board-level conversation.
I advise, write, and speak for the leaders navigating those same questions now. My perspective is practical, my assessments are honest, and I have no interest in theater.
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Most AI investments fail quietly — not from bad models, but from misaligned strategy and undefined ROI. I help leadership teams ask the right questions, set the right goals, and build the accountability structures that turn AI initiatives into measurable business outcomes.
Learn moreI design AI products with the full lifecycle in mind: not just what works technically, but what generates revenue, scales cost-effectively, and evolves as the business grows. From initial roadmap to production, I keep the business model in the room.
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A survey of how data quality has been defined, measured, and debated across decades of research — and why the field still lacks the practical, quantitative foundation modern AI demands.
Read essay →Data doesn't need to be perfect to be useful. It needs to be fit for a specific purpose. A framework for evaluating data quality against the use case that actually matters.
Read essay →How do you measure whether data is good enough to guide the distribution of critical resources? An applied case study in defining and quantifying data quality for a specific, consequential use case.
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