Sr. Staff Quantitative Product Researcher

Responsibilities

• Lead the development of user-centered metrics end-to-end: define constructs, design and validate survey instruments, establish reliability and sensitivity, guide experimentation plans, and partner with Data Science and Engineering to build behavioral proxies or predictive models.
• Extend measurement work into model stewardship, including retraining decisions, generalizability across user segments, and stress-testing model performance and failure modes.
• Conduct deep-dive learning analyses to identify drivers and levers behind key metrics and translate findings into actionable direction for product and Data Science partners.
• Set and execute the learning agenda across a broad problem space, prioritizing important questions and selecting appropriate methods such as surveys, behavioral analysis, quasi-experimental approaches, and modeling.
• Partner as an equal with Data Science and Engineering to shape measurement strategy and technical direction.
• Synthesize behavioral analyses, experiment results, qualitative insights, and research findings to inform product and business decisions.
• Present findings to senior leadership and tailor communication for executive audiences.
• Partner with research managers to set quantitative research direction and mentor junior quantitative researchers.

Requirements

• 7+ years of quantitative user research experience with large-scale consumer data.
• Track record of leading metric development end-to-end, from construct definition through survey validation, experimental validation, and behavioral proxy or predictive modeling.
• Deep expertise in survey methodology, including construct validity, reliability, and sensitivity.
• Expertise in statistical modeling, experimentation at scale, and behavioral analysis.
• Experience informing machine learning model evaluation and maintenance, including retraining triggers, generalizability across segments, and failure modes.
• Fluency in R or Python and SQL.
• Strong data visualization skills.
• Ability to operate as a peer to Data Science and Engineering and guide technical work.
• Proactive approach to identifying important questions and problems.
• Experience working cross-functionally, developing strategy with partners, and presenting to senior leadership.
• Experience leveraging AI tools in the research process with strong discernment and quality standards.

Nice-to-haves

• PhD in computational social science, economics, sociology, psychology, statistics, computer science, or a related field, or equivalent practical experience.

Compensation and Benefits

• Base salary: $163,639–$336,903 USD.
• Eligible for equity.
• Benefits information is available through Pinterest.
• US-based applicants only.
• No relocation assistance is available.