I combine behavioral data with user observation because what users do often tells a different story than what they say. 5+ years driving measurable product outcomes at Verizon and beyond.
"Sometimes the eye test when watching users use a product says more than the feedback they give."
I am a mixed-methods researcher - equally at home in a usability session, behavioral analytics, or a well-designed survey. What drives me is knowing whether a user could actually complete the task, not just whether they said they liked it.
I also find insights in data other teams have already seen but not fully read - buried in session replays, clickstream patterns, and behavioral analytics. And I know from experience that what users say they want and what they actually do rarely match. That gap is where the most interesting research lives.
Every study is different but I follow a consistent arc from problem framing to organizational impact.
I start by aligning with stakeholders on the actual research question - not the assumed one. The right question determines everything downstream.
I select methods based on what the question actually needs - not what is fastest or most familiar. Sometimes that means triangulating behavioral data with moderated sessions.
I watch what users do as closely as what they say. Whether rapid testing cycles using Maze for unmoderated testing, moderated sessions via UserTesting, or longer benchmark studies, the gap between behavior and stated preference is where the most important insight lives.
Insights only matter when they change something. I translate findings into developer-ready stories across every stage of the SDLC and follow through until the recommendation ships.
AI does not replace research judgment - it expands what is possible. Here is where it actually fits into how I work.
I use AI to scrape and analyze company reviews across the internet - app stores, Reddit, Trustpilot, Glassdoor - over defined time windows. This gives me a larger and more accurate picture of brand sentiment than any single source could provide, and it surfaces competitive insights that traditional survey methods miss entirely.
After qualitative sessions I use AI to help analyze interview transcripts, surface emerging themes, and generate initial persona frameworks from demographic patterns. What previously took a full day of manual affinity mapping can now be done in hours - freeing time for the higher-judgment work of pressure-testing and refining what the data actually means.
Before studies I use AI to help generate and refine discussion guides, screeners, and survey questions - using it as a thinking partner to pressure-test question framing and catch leading language before it reaches participants. This makes the prep work faster and the instruments sharper.
I use AI to generate storyboards and early wireframe concepts that I can bring into usability sessions faster. This is particularly useful for generative research where you want to put something tangible in front of users early - AI accelerates the artifact creation so research can happen sooner in the product cycle.
When research generates large volumes of unstructured data - open-ended survey responses, session notes, support ticket patterns, or unconventional sources like scraped reviews and usage logs - AI helps me organize and structure it into something actionable. The speed gain on information gathering and data organization means more time spent on the insight layer, not the sorting layer.
AI does not replace the judgment call - it handles the volume so I can focus on it. The most important parts of research are still deeply human: knowing which question to ask, reading the hesitation in a participant's voice, deciding what a finding actually means for the product. AI gets me to that moment faster.
The platforms and practices I reach for most - across research operations, behavioral analytics, and AI-assisted synthesis.
Selected research programs spanning enterprise telecom and consumer experience, each with a distinct challenge, method mix, and measurable outcome.
Generative and evaluative research across BYOD and New Customer flows that surfaced pain points and shaped the product roadmap.
End-to-end accessibility audits across 3 core flows and 11+ entry points, delivering remediation roadmaps adopted by engineering.
How research, data analytics, and cross-functional leadership combined to drive $100M+ in revenue on Verizon's highest-traffic page.
A 0-1 consumer research engagement combining hallway testing, community surveys, and concept validation to fix a hidden checkout problem costing a local cafe direct revenue.
From directors, product managers, and engineers who worked with me directly at Verizon.
Open to contract and full-time roles. Hybrid or remote, NJ and NYC area preferred.