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AI Product Strategy · Healthcare Research · Systems Engineering
I sit at the intersection of research rigor, technical depth, and product strategy — and that combination is rare.
Most AI product leaders come from design, engineering, or business. My path ran through clinical research, population health analytics, and hands-on AI systems engineering. That background shapes everything about how I approach AI products: methodically, with an eye toward governance, and with the operator instincts to know what actually ships.
The career narrative
This is the one section meant to be read, not scanned.
I started in life sciences — B.S. in Human Biology at Duquesne, M.S. in Exercise Science at Bloomsburg where my thesis work involved EMG signal analysis across resistance training conditions. That sounds far from AI product strategy, but it built something essential: a deep respect for research methodology, measurement validity, and what it actually means to draw defensible conclusions from data. I learned to design studies, run statistical analyses, and communicate findings. That foundation has never left me.
From there I moved into healthcare operations and clinical research management at Main Line Health — eventually as Project Manager on a Phase III investigator-initiated IND trial, coordinating FDA submissions, managing research staff, and overseeing a portfolio of seventeen resident research projects simultaneously. I added an MBA at night during this period to understand the organizational and strategic layer, not just the operational one. By the time I joined Breastcancer.org in 2022, I could hold a rigorous conversation about study design, budget management, and product prioritization in the same meeting — and increasingly, about what AI could and couldn't safely do inside a patient-facing organization.
At Breastcancer.org I came in as Associate Director of Research — directing the annual patient research program, building analytics infrastructure in AWS, designing survey instruments, and producing the quantitative reporting that feeds board strategy. But the role expanded, and so did the work. I started leading product discovery for AI-enabled digital health tools: defining MVPs, establishing governance guardrails, facilitating alignment between technology, content, and medical review teams, and building the systems that would actually make AI safe to deploy in a context where patients are making treatment decisions. Somewhere in that process I went from planning AI systems to building them — production agents, agentic pipelines, parallel orchestration workflows, MCP integrations. I now do both, and I think that's the point.
What this looks like in practice
The combination shows up in consistent patterns.
Most AI initiatives fail not because the technology doesn't work, but because no one did the hard work of defining what “working” means in context — including what the system must never do. My research background means I'm wired to start with hypotheses, success criteria, and failure modes before I write a single prompt.
I'm fluent in SQL and Python, work in AWS, and build production AI agents — but I can also write the board-level framing, facilitate the cross-functional alignment meeting, and define the pilot evaluation criteria. Most people who can do one of those things can't do the others. I can move across all three in the same day.
Breastcancer.org reaches millions of patients navigating active treatment. That's not a context where you deploy fast and fix it later. My experience designing AI products there — sourcing transparency requirements, hard guardrails against clinical interpretation, escalation pathways, medical review workflows — is directly applicable anywhere AI is touching something that actually matters.
Vision Venture is where I run experiments. The agentic delivery systems, GEO audit intelligence platforms, and content operations pipelines I've built there aren't demos — they're production systems I use and iterate on. It's where the gap between “I understand this conceptually†and “I've debugged this at 11pm†closes.
Credentials & background snapshot
Quick highlights.
Background at a glance
Career timeline.
What I'm looking for next
Personal direction and fit.
I'm actively exploring Director and Senior PM roles in AI product, digital health, and innovation strategy — particularly at organizations where the work is genuinely consequential and where someone who can hold both the research rigor and the technical implementation simultaneously is an asset rather than an edge case.
I'm drawn to organizations building AI products where safety and trust are real constraints, not marketing language. Healthcare, clinical decision support, regulated industries, mission-driven orgs. Places where “move fast and fix it later” isn't an option and where thoughtful product governance is a competitive advantage.
If you're evaluating whether my background fits a role you're hiring for, the case studies on this site will tell you more than a resume will. They show how I think, what I build, and the tradeoffs I navigate — not just what I've done.
The case studies show thinking, structure, governance, and outcomes — more than a resume ever will.