Director of Data Science, Ads Measurement & Attribution

Lead the end-to-end science strategy for ads measurement and attribution across on-platform, off-platform, and partner surfaces. Build a coherent framework integrating incrementality testing, causal inference, calibrated attribution, MMM, and geo experimentation, while championing privacy-centric methodologies (clean rooms, aggregation, differential privacy). Design and govern lift studies, develop standard experiment patterns, power calculators, guardrails, and diagnostics. Develop causal estimators (e.g., CUPED, DR/DML, synthetic controls) and variance reduction techniques to accelerate signal. Advance attribution approaches robust to cookie deprecation, ATT, and cross-device fragmentation; partner with Eng to productionize calibrated models that reconcile observational and experimental evidence; define success metrics and calibration protocols. Shape the measurement product roadmap in collaboration with Product; translate science into advertiser-facing solutions and narratives; ensure compliance with privacy regulations; hire, mentor, and develop a diverse DS team; set standards for code quality, experimentation hygiene, documentation, and peer review. Represent thought leadership in customer conversations, industry forums, and with measurement partners and clean-room providers; contribute to publications and internal tech talks that raise the scientific bar.