Director of Data Science, Ads Measurement & Attribution at PRINTEREST INC

Seattle, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

04 Feb, 26

Salary

466620.0

Posted On

06 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Statistics, Applied ML, Ads Measurement, Attribution, Experimentation, Causal Inference, A/B Testing, Python, R, SQL, Privacy-Centric Measurement, Team Building, Cross-Functional Leadership, Communication Skills, Product Mindset, Metric Design

Industry

Software Development

Description

About Pinterest: 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. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. 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. What you’ll do: Vision and strategy Define and drive the end-to-end science strategy for ads measurement and attribution across on-platform, off-platform, and partner surfaces. Establish a coherent framework that integrates incrementality testing, causal inference, calibrated attribution, MMM, and geo experimentation. Champion privacy-centric methodologies (e.g., clean rooms, aggregation, differential privacy, conversion modeling under signal loss). Causal measurement and experimentation Lead the design and governance of lift studies where merchants run A/B tests to estimate lift and guide investment decisions. Build standardized experiment design patterns, power calculators, guardrails, and experiment-quality diagnostics. Develop causal estimators (e.g., CUPED, DR/DML, synthetic controls) and variance reduction techniques to improve sensitivity and speed to signal. Attribution and modeling Evolve our multi-touch and data-driven attribution approaches to be durable with cookie deprecation, ATT, SKAN, and cross-device fragmentation. Partner with Eng to productionize calibrated models that reconcile observational and experimental evidence; define success metrics and calibration protocols. Advance conversion modeling, identity-resilient matching, and probabilistic methods where deterministic signals are sparse. Product and cross-functional leadership Partner with Product and Engineering to shape the measurement product roadmap; translate science into advertiser-facing solutions and clear narratives. Collaborate with Sales, Marketing Science, and Partnerships to position our methods with advertisers and measurement partners. Engage with Legal/Privacy to ensure compliance and responsible AI practices across data usage and modeling. Team building and talent development Hire, lead, and mentor a diverse team of DS managers and senior ICs; foster a culture of scientific rigor, reproducibility, and impact. Set standards for code quality, experimentation hygiene, documentation, and peer review across the DS org. Influence and external representation Represent Pinterest science in customer briefings, industry forums, and with third-party measurement partners and clean-room providers. Contribute to publications, whitepapers, and internal tech talks that raise the scientific bar. What we’re looking for: Leadership and experience 10+ years of experience in data science, statistics, or applied ML, with substantial time in ads measurement, attribution, or experimentation at scale. 5+ years leading DS teams, including managing managers and senior ICs; proven track record of building high-performing, inclusive teams. Demonstrated success driving cross-functional impact with Product, Engineering, Sales, and Legal/Privacy. Exceptional communication skills—able to explain complex methods to executives and customers and to translate business needs into scientific work. Technical and methodological depth Strong foundation in causal inference and experimentation (A/B testing, geo experiments, quasi-experimental designs, variance reduction). Hands-on experience with attribution and calibration (rule-based and data-driven MTA, counterfactual estimation, aggregation and identity challenges). Familiarity with MMM and triangulation approaches that reconcile MMM, MTA, and lift tests. Proficiency in Python or R; strong SQL; comfort reviewing production code and collaborating with platform/ML engineers. Experience with privacy-centric measurement: clean rooms, aggregation frameworks, differential privacy, on-device/edge signals, and privacy regulations. Product mindset Evidence of turning science into durable, scalable products with clear customer value. Fluency in metric design, north-star definitions, and guardrails that align with advertiser outcomes. Experience with ad platforms, retail media, or e-commerce measurement. Knowledge of identity resolution, SKAN/ATT, and browser privacy changes. Publications or talks in causal inference, experimentation, or ads measurement. Advanced degree in Statistics, Econometrics, CS, or related quantitative field. In-Office Requirement Statement: We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection. This role will need to be in the office for in-person collaboration 1-2 times/week in the San Francisco, Palo Alto, or Seattle offices. Relocation Statement: This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. #LI-NM4 At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $226,644—$466,620 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.

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Responsibilities

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, while leading a high-performing team of data scientists and analysts.