Director of Data Science, Ads Measurement & Attribution

Pinterest
San Francisco, CA, US; Seattle, WA, USPosted 4 March 2026

Job Description

<div class="content-intro"><p><strong>About Pinterest:</strong></p> <p>Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.</p> <p>Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the <a href="https://www.pinterestcareers.com/our-life/pinflex/">flexibility</a> to do your best work. Creating a career you love? It’s Possible.</p></div><p> </p> <p>As the Director of Data Science for Ads Measurement Attribution, you will set the vision and lead the science strategy behind how advertisers understand the value of Pinterest. You’ll own the roadmap for causal measurement, attribution, and incrementality—spanning first- and third-party solutions, experiment design (including incrementality studies), and model innovation that is privacy-safe and aligned with evolving industry standards. You’ll grow and lead a high-performing team of data scientists and analysts, partner tightly with Eng and Product, and represent Pinterest science externally with customers and the ecosystem.</p> <p> </p> <p><strong>What you’ll do:</strong></p> <ul> <li>Vision and strategy</li> <ul> <li>Define and drive the end-to-end science strategy for ads measurement and attribution across on-platform, off-platform, and partner surfaces.</li> <li>Establish a coherent framework that integrates incrementality testing, causal inference, calibrated attribution, MMM, and geo experimentation.</li> <li>Champion privacy-centric methodologies (e.g., clean rooms, aggregation, differential privacy, conversion modeling under signal loss).</li> </ul> <li>Causal measurement and experimentation</li> <ul> <li>Lead the design and governance of lift studies where merchants run A/B tests to estimate lift and guide investment decisions.</li> <li>Build standardized experiment design patterns, power calculators, guardrails, and experiment-quality diagnostics.</li> <li>Develop causal estimators (e.g., CUPED, DR/DML, synthetic controls) and variance reduction techniques to improve sensitivity and speed to signal.</li> </ul> <li>Attribution and modeling</li> <ul> <li>Evolve our multi-touch and data-driven attribution approaches to be durable with cookie deprecation, ATT, SKAN, and cross-device fragmentation.</li> <li>Partner with Eng to productionize calibrated models that reconcile observational and experimental evidence; define success metrics and calibration protocols.</li> <li>Advance conversion modeling, identity-resilient matching, and probabilistic methods where deterministic signals are sparse.</li> </ul> <li>Product and cross-functional leadership</li> <ul> <li>Partner with Product and Engineering to shape the measurement product roadmap; translate science into advertiser-facing solutions and clear narratives.</li> <li>Collaborate with Sales, Marketing Science, and Partnerships to position our methods with advertisers and measurement partners.</li> <li>Engage with Legal/Privacy to ensure compliance and responsible AI practices across data usage and modeling.</li> </ul> <li>Team building and talent development</li> <ul> <li>Hire, lead, and mentor a diverse team of DS managers and senior ICs; foster a culture of scientific rigor, reproducibility, and impact.</li> <li>Set standards for code quality, experimentation hygiene, documentation, and peer review across the DS org.</li> </ul> <li>Influence and external representation</li> <ul> <li>Represent Pinterest science in customer briefings, industry forums, and with third-party measurement partners and clean-room providers.</li> <li>Contribute to publications, whitepapers, and internal tech talks that raise the scientific bar.</li> </ul> </ul> <p> </p> <p><strong>What we’re lookin ... (truncated, view full listing at source)