Quantitative UX Researcher

Roles & Responsibilities

• 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

• 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

• 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