Lead segmentation and persona development grounded in quantitative evidence to inform product strategy and design decisions
Design and execute structured studies (surveys, experiments, card sorts, structural evaluations) with rigor in sampling, instrument design, and bias mitigation
Establish measurement frameworks for controlled studies, defining constructs, scales, success metrics, and guardrails aligned to product questions
Analyze drivers of experience and outcomes across segments using multivariate analysis and regression modeling
Support experimentation and longitudinal research, applying repeated-measures or within-subject designs and translating findings into actionable recommendations
Influence cross-functional teams with clear, decision-ready narratives that connect analytical findings to product implications in complex technical domains
Help mature the quantitative research practice, establishing scalable approaches and elevating analytic standards across business lines
Required Technical / Functional Skills
Extensive experience in quantitative UX research with applied product environments, including segmentation and persona development
Expertise in survey methodology: construct/scale selection, sampling, weighting, questionnaire design, and pre-testing for reliability and bias
Strong command of multivariate analysis, regression modeling, and repeated-measures/longitudinal designs
Proficiency in Python or R for data manipulation, modeling, and analysis; strong SQL skills for structured data extraction
Ability to independently translate ambiguous product or design questions into structured study plans, analytic approaches, and actionable insights
Familiarity with Qualtrics (survey programming, branching logic, quality controls) and Glassbox (behavioral journey insights for segmentation and targeting)
Excellent communication skills, with the ability to tailor insights to Product Managers, Designers, Engineers, and Data Scientists
Desired Skills / Experience
Experience applying segmentation insights to information architecture, workflow design, and experimentation strategies
Ability to triangulate attitudinal, behavioral, and session-level data to validate segments and identify drivers of experience
Experience in regulated, data-dense environments such as financial services, analytics platforms, or high-stakes B2B tools
Demonstrated autonomy, prioritization in ambiguous spaces, and ability to influence senior stakeholders through evidence-based insights
Exposure to generative AI tools and techniques for research, including automated data synthesis, natural language summarization, and predictive modeling