Sr. Staff Quantitative Product Researcher

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Pinterest is looking for a Sr. Staff Quantitative Product Researcher to lead the development and evolution of user-centered measurement across our consumer experience. As the volume of measurement asks grows, we need a seasoned researcher who can partner with Data Science and Engineering as an equal — not just contributing to metrics work, but guiding it end-to-end and shaping the direction of how we measure the Pinner experience.

In this role, you'll own the full arc of measurement work: identifying what we should be measuring, designing surveys that capture the constructs that matter, validating that our metrics are reliable and sensitive, guiding experimentation plans to prove they can move, and partnering with DS and Eng to build behavioral proxies or predictive models where they're needed. You'll also extend this work into how we evaluate and maintain the systems that depend on these metrics — informing when models need retraining, whether they generalize across user segments, and what levers actually move the outcomes we care about. You'll bring expertise in behavioral analysis, statistical modeling, survey methodology, and experimentation to strategic questions on the holistic Pinner experience, and you'll present findings to senior audiences and partner with leaders to turn learnings into strategy.

• Lead the development of user-centered metrics end-to-end: define the construct, design and validate the survey instrument, establish reliability and sensitivity, guide the experimentation plan to prove the metric can move, and partner with DS and Eng to build behavioral proxies or predictive models when appropriate

• Extend measurement work into model stewardship — inform retraining decisions, evaluate generalizability across user segments, and stress-test where models do and don't hold up

• Conduct deep-dive learning analyses to uncover the drivers and levers behind key metrics, and translate those into actionable direction for product and DS partners

• Set and execute the learning agenda across a broad problem space — prioritize the questions that matter most, choose the right methods (surveys, behavioral analysis, quasi-experimental approaches, modeling), and work on the right project at the right time

• Show up as an equal partner to Data Science and Engineering, shaping the direction of measurement work rather than executing against someone else's spec

• Synthesize across behavioral analyses, experiment results, qualitative insights, and your own work