Director of Data Science, Ads Measurement & Attribution
Director of Data Science, Ads Measurement & Attribution
Job Description
Responsibilities
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
Qualifications
- 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.
- 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.
- 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.
Preferred Qualifications
- 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.
Locations
- San Francisco, CA, US; Seattle, WA, US
Salary
269,306 - 471,286 USD / yearly
Estimated Salary Rangemedium confidence
250,000 - 400,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Causal inference and experimentation (A/B testing, geo experiments, quasi-experimental designs, variance reduction)intermediate
- Attribution and calibration (rule-based and data-driven MTA, counterfactual estimation, aggregation and identity challenges)intermediate
- MMM and triangulation approachesintermediate
- Python or Rintermediate
- SQLintermediate
- Reviewing production codeintermediate
- Collaborating with platform/ML engineersintermediate
- Privacy-centric measurement: clean rooms, aggregation frameworks, differential privacy, on-device/edge signals, privacy regulationsintermediate
- Metric design and north-star definitionsintermediate
- Cross-functional leadership with Product, Engineering, Sales, Legal/Privacyintermediate
- Communication of complex methods to executives and customersintermediate
Required Qualifications
- 10+ years of experience in data science, statistics, or applied ML, with substantial time in ads measurement, attribution, or experimentation at scale. (experience)
- 5+ years leading DS teams, including managing managers and senior ICs; proven track record of building high-performing, inclusive teams. (experience)
- Demonstrated success driving cross-functional impact with Product, Engineering, Sales, and Legal/Privacy. (experience)
- Exceptional communication skills—able to explain complex methods to executives and customers and to translate business needs into scientific work. (experience)
- Strong foundation in causal inference and experimentation (A/B testing, geo experiments, quasi-experimental designs, variance reduction). (experience)
- Hands-on experience with attribution and calibration (rule-based and data-driven MTA, counterfactual estimation, aggregation and identity challenges). (experience)
- Familiarity with MMM and triangulation approaches that reconcile MMM, MTA, and lift tests. (experience)
- Proficiency in Python or R; strong SQL; comfort reviewing production code and collaborating with platform/ML engineers. (experience)
- Experience with privacy-centric measurement: clean rooms, aggregation frameworks, differential privacy, on-device/edge signals, and privacy regulations. (experience)
- Evidence of turning science into durable, scalable products with clear customer value. (experience)
- Fluency in metric design, north-star definitions, and guardrails that align with advertiser outcomes. (experience)
Preferred Qualifications
- Experience with ad platforms, retail media, or e-commerce measurement. (experience)
- Knowledge of identity resolution, SKAN/ATT, and browser privacy changes. (experience)
- Publications or talks in causal inference, experimentation, or ads measurement. (experience)
- Advanced degree in Statistics, Econometrics, CS, or related quantitative field. (experience)
Responsibilities
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
Tags & Categories
Director of Data Science, Ads Measurement & Attribution
Director of Data Science, Ads Measurement & Attribution
Job Description
Responsibilities
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
Qualifications
- 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.
- 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.
- 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.
Preferred Qualifications
- 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.
Locations
- San Francisco, CA, US; Seattle, WA, US
Salary
269,306 - 471,286 USD / yearly
Estimated Salary Rangemedium confidence
250,000 - 400,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Causal inference and experimentation (A/B testing, geo experiments, quasi-experimental designs, variance reduction)intermediate
- Attribution and calibration (rule-based and data-driven MTA, counterfactual estimation, aggregation and identity challenges)intermediate
- MMM and triangulation approachesintermediate
- Python or Rintermediate
- SQLintermediate
- Reviewing production codeintermediate
- Collaborating with platform/ML engineersintermediate
- Privacy-centric measurement: clean rooms, aggregation frameworks, differential privacy, on-device/edge signals, privacy regulationsintermediate
- Metric design and north-star definitionsintermediate
- Cross-functional leadership with Product, Engineering, Sales, Legal/Privacyintermediate
- Communication of complex methods to executives and customersintermediate
Required Qualifications
- 10+ years of experience in data science, statistics, or applied ML, with substantial time in ads measurement, attribution, or experimentation at scale. (experience)
- 5+ years leading DS teams, including managing managers and senior ICs; proven track record of building high-performing, inclusive teams. (experience)
- Demonstrated success driving cross-functional impact with Product, Engineering, Sales, and Legal/Privacy. (experience)
- Exceptional communication skills—able to explain complex methods to executives and customers and to translate business needs into scientific work. (experience)
- Strong foundation in causal inference and experimentation (A/B testing, geo experiments, quasi-experimental designs, variance reduction). (experience)
- Hands-on experience with attribution and calibration (rule-based and data-driven MTA, counterfactual estimation, aggregation and identity challenges). (experience)
- Familiarity with MMM and triangulation approaches that reconcile MMM, MTA, and lift tests. (experience)
- Proficiency in Python or R; strong SQL; comfort reviewing production code and collaborating with platform/ML engineers. (experience)
- Experience with privacy-centric measurement: clean rooms, aggregation frameworks, differential privacy, on-device/edge signals, and privacy regulations. (experience)
- Evidence of turning science into durable, scalable products with clear customer value. (experience)
- Fluency in metric design, north-star definitions, and guardrails that align with advertiser outcomes. (experience)
Preferred Qualifications
- Experience with ad platforms, retail media, or e-commerce measurement. (experience)
- Knowledge of identity resolution, SKAN/ATT, and browser privacy changes. (experience)
- Publications or talks in causal inference, experimentation, or ads measurement. (experience)
- Advanced degree in Statistics, Econometrics, CS, or related quantitative field. (experience)
Responsibilities
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
Tags & Categories