About
The homepage leads with outcomes; this page is the longer conversation. I am a principal product manager who has shipped in lean startups, scaling ecommerce, and large regulated utilities, and the habits are the same: own the scoreboard, keep discovery and data in the same room, and do not let the roadmap drift from what crews, customers, or finance actually see. I have run multi-quarter programs across engineering, design, vendors, operations, and go-to-market. I prioritize with discovery, web analytics, dashboards, funnels, cohorts, and experiments—alongside what teams see in the field. When marketing and revenue matter, I keep acquisition, conversion, and lifecycle in the same loop as delivery. I use AI and LLMs heavily in my own work—research, scenarios, specs, prototyping—and in product only when there is a clear use case, evaluation, and a credible path to production, including permissions, data boundaries, human review, and rollback. I am most useful when the situation is messy, the stakes are high, and better decisions earlier would materially change the outcome.
Interests outside work that still inform how I read tech, risk, and timing.
Outside work I follow cars and automotive tech, artificial intelligence, science that challenges assumptions, robotics and drones, and serious consumer and industrial tech. It is personal, but it keeps me grounded about what is hard to build, what is ethically loaded, and what is actually ready to deploy—not only what is trending.
Beliefs that hold whether we are measuring conversion, cycle time, or storm restoration.
Where the same skills from the case studies usually matter: messy scope, lots of stakeholders, and limited room to miss on the bet.
How startup, scale-up, and enterprise contexts shaped how I work.
Read in order, those chapters stack—they are not contradictory. Startups taught me speed and how to ship with incomplete information. Scaling companies taught me alignment and how to make process serve outcomes. Enterprise and regulated work taught me dense stakeholders, vendor reality, and how to keep delivery credible when requirements multiply. DTC and marketing-heavy roles taught me how fast funnel and SEO reality punish vague roadmaps, and how to keep analytics, reporting, and brand in the same loop as engineering. LLMs help most when they shorten the path from hypothesis to evidence; they help least when nobody owns quality, safety, and adoption. That range makes it easier to spot the same failure modes in different packaging, which is why I can walk into a new industry and find the seams quickly.