Best-fit roles
- Senior / Staff Quantitative UX Researcher
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- Mixed Methods UX Researcher with strong quant depth
- Research Scientist-adjacent UXR for product teams
Quantitative User Experience Research
I help teams turn behavioral data into better product decisions.
I lead quantitative user experience research that combines behavioral research, statistics, experimentation, and product thinking. This portfolio highlights confidentiality-safe summaries of work spanning multi-step journeys, trust-sensitive decisions, segmentation systems, and workflow design.
For hiring teams
Where I can create value fastest
I fit teams that need more than interview synthesis: teams with messy product questions, behavioral data, survey needs, AI evaluation risk, or high-stakes roadmap decisions that require careful measurement.
Methods
Tools
Decision impact
- Turn broad UX questions into measurable constructs.
- Connect product behavior to user motivation and friction.
- Quantify tradeoffs so teams can prioritize with confidence.
- Evaluate AI systems as human-facing product experiences.
About
Statistical depth when needed. Strategic breadth when it matters.
My work sits at the overlap of behavioral research, statistics, and product decision-making. I use quantitative UX research to help teams understand users, workflows, and product behavior well enough to make better decisions.
That means bringing statistical depth when the problem calls for analytics, predictive modeling, experimentation, or measurement rigor, and statistical breadth when the goal is customer insight, rapid feedback, or strategy impact.
What I optimize for
- Questions anchored to real product and business decisions
- Methods matched to uncertainty rather than habit
- Behavioral data combined with survey and workflow evidence
- Outputs that survive handoff into roadmap and design work
Personal
Faith, endurance, service, and wild places shape how I work.
The same habits I bring to research show up outside work: long horizons, careful preparation, humility in hard environments, and a bias toward service. These photos are a more personal view of the places and practices that keep me grounded.
“Thy word is a lamp unto my feet, and a light unto my path.”
Psalm 119:105
My work is shaped by service, stewardship, humility, and care for people. Those values are rooted in my Christian faith, and I try to express them in ways that are accessible in secular, scientific, and multidisciplinary settings.
Outside research, I am drawn to mountains, trail running, climbing, volunteering, and endurance work. They teach patience, risk assessment, attention, recovery, and respect for conditions that are bigger than you. That is also how I try to approach complicated product and research work.
Personal anchors
- Christian faith and service as a practical orientation to people
- Trail running and mountain travel as training in endurance and judgment
- Volunteer work in outdoor communities and public lands
- Technology work guided by human dignity, agency, and responsibility
Selected engagements
Representative Research Engagements
A selection of anonymized engagements spanning product strategy, trust, conversion, segmentation, and adaptive experience design. Details are intentionally generalized to respect client confidentiality while showing the kinds of problems, methods, and outcomes I help teams navigate.
Toolkit
Methods I use for customer insight, rapid feedback, and strategy impact
Methods are useful when they help teams see clearly, choose responsibly, and keep moving.
Behavioral telemetry and workflow analysis
Clickstream analysis, journey diagnosis, benchmarking, and behavioral pattern detection to show how product use actually unfolds in the field.
Survey measurement and latent constructs
Survey design, scale development, preference measurement, validation work, and latent-construct modeling for questions that are not directly observable.
Segmentation, clustering, and classification
Segmentation systems grounded in observed behavior, profiling, classification, and measurable product implications rather than static archetypes.
Experimentation, prioritization, and predictive modeling
Experimental thinking, feature prioritization, predictive modeling, and decision-ready synthesis that help teams choose where to invest next.
Process
A repeatable path from ambiguity to evidence-backed action
Process matters most when the route is uncertain and the team still has to decide.
Frame the decision
Clarify the business risk, the product question, and what would actually change if the team learned something new.
Choose the right level of statistical depth
Match the method to the problem, whether that means experimentation, telemetry analysis, survey measurement, segmentation, or a blended design.
Integrate behavior, measurement, and context
Bring together behavioral data, workflow signals, and well-structured research measures so patterns are interpretable rather than just descriptive.
Turn analysis into operating decisions
Translate findings into prioritization frameworks, scorecards, segment definitions, recommendations, and roadmap inputs that teams can actually use.