About

I lead the design of decision systems and applied AI inside scientific organizations. The day job is Associate Director of Data Science & Product Management at Montai Therapeutics. The training is a PhD in multi-omics analysis.

The path from bench science to product wasn’t planned. The transferable skill turned out to be designing decision systems under uncertainty — figuring out what counts as evidence, which experiment is worth the cost, and how to keep a team’s confidence calibrated when truth comes back late or partial. PhD work taught those moves; drug discovery ML uses them every day.

The questions changed. Which model architecture balances precision vs. recall for this drug target? replaced Which experimental condition will produce the cleanest signal? The decision-making register stayed the same: first-principles framing of what counts as evidence, explicit tradeoffs, and a strong allergy to metric theater.

A pattern I keep noticing in data and AI leadership: people tend to be strong on product intuition or technical depth, with leadership glued in afterward. The hybrid — product judgment, technical depth, and research training in the same person — is rarer than it should be. It’s where I do my best work.

I write about decision intelligence, evaluation under uncertainty, and how scientific organizations actually adopt AI. I publish skills, playbooks, and field notes from the workflows I actually use, with the goal of keeping the system that runs my work in the open — so the next session opens on something that already worked.

If any of that resonates, LinkedIn and the Advisory page are good places to keep going.