I work where product decisions are still fluid—before execution takes over and before ambiguity quietly becomes risk. This is the moment where getting it wrong carries real cost.
My work operates upstream of delivery. I reframe questions so the real problem becomes legible, surface tradeoffs teams haven’t named yet, and translate uncertainty into evidence strong enough to shape direction. I don’t eliminate ambiguity; I make it productive. Much of my work operates inside AI-driven and regulated systems, where decisions are rarely abstract and consequences are never neutral. In these contexts, acting too early or too late can create real harm — for people, companies, and the systems they rely on.
For four years at Amazon, I worked across healthcare benefits for ~1.5M employees, in domains where people make consequential decisions based on what systems surface or obscure. My research shifted roadmaps, informed backend architecture, and reframed AI adoption as an accountability problem rather than a technical one.I use research, design, and systems-thinking to frame the right questions, surface tradeoffs, and make uncertainty visible early enough to change direction. Rather than optimizing for speed alone, I design systems that make decisions safer to take, easier to explain, and accountable to the people affected by them.
Currently, I'm building BetCheck, a diagnostic tool that makes product assumptions visible and testable before teams commit resources.
