Domain
01
Studies that convert high-volume, ambiguous data — surveys, telemetry, revenue — into the specific number a team used to make a real decision: a pricing call, a Q1 priority, a go/no-go on a roadmap bet.
02
Systems built once and reused for years — shared taxonomies, standardized metrics, and infrastructure that replaced one-off studies with something the whole org could measure against.
03
Research that treated AI features as products to be adopted, not technology to be marveled at — starting from what job someone was trying to get done, and letting that decide what “success” meant.
Approach
More than any single project, my work is driven by how I tackle ambiguous spaces—stripping away the noise to find the core signal. I don't just research product teams; I think like a product person. That means taking shared ownership of business metrics, marketing strategy, and technical realities so stakeholders become active research partners. I move comfortably between working with engineers and aligning with VPs, always holding two priorities in balance: creating durable, long-term frameworks while staying ready to solve the urgent, time-sensitive problems that keep decisions moving.
Selected work
Open to collaborations on AI product surfaces, analytics narratives, and workflow tools.