Director of Data Science, Ads Delivery & Performance
Director of Data Science, Ads Delivery & Performance
Job Description
Responsibilities
- Define the multi-year vision and roadmap for Ads Delivery & Performance across all Pinterest surfaces, aligning to company and Ads org priorities.
- Establish clear north-star outcomes (e.g., long-term revenue quality, retained advertiser value, Pinner experience) and the right cascade of input metrics and guardrails that drive them.
- Identify, define, and instrument the input metrics that most influence delivery and performance, such as supply health and coverage, eligibility and match rate, win rate, fill rate, pacing and spend smoothness, auction/bid competitiveness, latency/SLA adherence, ranking/model freshness and coverage, feature quality, CTR/CVR leading indicators, frequency and ad load distribution, quality/relevance signals, and policy/trust frictions.
- Build metric specs, ownership, alerting, and weekly business reviews so that teams operate to inputs (and understand their elasticities to outcomes like RPM, ROAS, advertiser retention, and Pinner satisfaction).
- Hire, develop, and inspire a diverse, high-performance organization of data science managers and senior ICs; set crisp role definitions, growth paths, and succession planning.
- Establish rigorous standards for experimentation, causal inference, and production analytics; ensure consistently trustworthy, decision-grade outputs.
- Work hand-in-hand with Product and Engineering on marketplace, ranking, pacing, and quality systems (e.g., auction design, supply/demand balancing, relevance, cold-start, ads load/frequency, shopping ads delivery).
- Translate ambiguous business questions into structured analyses and roadmaps; ensure the org moves from insight to shipped change with measurable impact.
- Own the measurement strategy for ads quality and delivery: counterfactual measurement, lift and incrementality, experiment design, long-term value, and guardrail monitoring for Pinner experience.
- Improve our decision frameworks, define minimum statistical standards, and drive consistency in A/B testing, quasi-experiments, and observational methods when experiments are impractical.
- Present complex analytical findings to executives and cross-functional partners with clarity; drive alignment on trade-offs across revenue, advertiser value, and Pinner experience.
- Create transparent operating cadences (QBRs/MBRs, input metric reviews, launch reviews) that keep teams focused on the levers that matter.
Qualifications
- MS or PhD in a quantitative field (Computer Science, Statistics, Math, Engineering, Economics or related) or equivalent practical experience.
- 10+ years in Data Science, Algorithmic Engineering, or Machine Learning, with significant impact in digital advertising or large-scale marketplaces.
- 5+ years directly managing data science organizations, including managing managers and scaling multi-team groups in a technology company.
- Deep experience with statistical analysis and causal inference, experiment design, and measurement for complex marketplaces.
- Deep experience with production analytics and large-scale data tooling (e.g., Python/R, SQL; Spark/Hive or similar).
- Deep experience with applied ML or relevance/ranking systems; familiarity with auction dynamics, pacing, bidding, and quality/relevance modeling.
- Proven track record of establishing and operating through input metrics tied to business outcomes.
- Excellent written and verbal communication; adept at influencing senior cross-functional partners and executives.
Locations
- Seattle, WA, US; Remote, US (Remote)
Salary
242,634 - 499,541 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
- Statistical analysis and causal inferenceintermediate
- Experiment design and measurement for complex marketplacesintermediate
- Production analyticsintermediate
- Large-scale data tooling (Python/R, SQL, Spark/Hive)intermediate
- Applied MLintermediate
- Relevance/ranking systemsintermediate
- Auction dynamicsintermediate
- Pacingintermediate
- Biddingintermediate
- Quality/relevance modelingintermediate
- Input metrics tied to business outcomesintermediate
- Written and verbal communicationintermediate
- Influencing senior cross-functional partners and executivesintermediate
Required Qualifications
- MS or PhD in a quantitative field (Computer Science, Statistics, Math, Engineering, Economics or related) or equivalent practical experience. (experience)
- 10+ years in Data Science, Algorithmic Engineering, or Machine Learning, with significant impact in digital advertising or large-scale marketplaces. (experience)
- 5+ years directly managing data science organizations, including managing managers and scaling multi-team groups in a technology company. (experience)
- Deep experience with statistical analysis and causal inference, experiment design, and measurement for complex marketplaces. (experience)
- Deep experience with production analytics and large-scale data tooling (e.g., Python/R, SQL; Spark/Hive or similar). (experience)
- Deep experience with applied ML or relevance/ranking systems; familiarity with auction dynamics, pacing, bidding, and quality/relevance modeling. (experience)
- Proven track record of establishing and operating through input metrics tied to business outcomes. (experience)
- Excellent written and verbal communication; adept at influencing senior cross-functional partners and executives. (experience)
Responsibilities
- Define the multi-year vision and roadmap for Ads Delivery & Performance across all Pinterest surfaces, aligning to company and Ads org priorities.
- Establish clear north-star outcomes (e.g., long-term revenue quality, retained advertiser value, Pinner experience) and the right cascade of input metrics and guardrails that drive them.
- Identify, define, and instrument the input metrics that most influence delivery and performance, such as supply health and coverage, eligibility and match rate, win rate, fill rate, pacing and spend smoothness, auction/bid competitiveness, latency/SLA adherence, ranking/model freshness and coverage, feature quality, CTR/CVR leading indicators, frequency and ad load distribution, quality/relevance signals, and policy/trust frictions.
- Build metric specs, ownership, alerting, and weekly business reviews so that teams operate to inputs (and understand their elasticities to outcomes like RPM, ROAS, advertiser retention, and Pinner satisfaction).
- Hire, develop, and inspire a diverse, high-performance organization of data science managers and senior ICs; set crisp role definitions, growth paths, and succession planning.
- Establish rigorous standards for experimentation, causal inference, and production analytics; ensure consistently trustworthy, decision-grade outputs.
- Work hand-in-hand with Product and Engineering on marketplace, ranking, pacing, and quality systems (e.g., auction design, supply/demand balancing, relevance, cold-start, ads load/frequency, shopping ads delivery).
- Translate ambiguous business questions into structured analyses and roadmaps; ensure the org moves from insight to shipped change with measurable impact.
- Own the measurement strategy for ads quality and delivery: counterfactual measurement, lift and incrementality, experiment design, long-term value, and guardrail monitoring for Pinner experience.
- Improve our decision frameworks, define minimum statistical standards, and drive consistency in A/B testing, quasi-experiments, and observational methods when experiments are impractical.
- Present complex analytical findings to executives and cross-functional partners with clarity; drive alignment on trade-offs across revenue, advertiser value, and Pinner experience.
- Create transparent operating cadences (QBRs/MBRs, input metric reviews, launch reviews) that keep teams focused on the levers that matter.
Tags & Categories
Director of Data Science, Ads Delivery & Performance
Director of Data Science, Ads Delivery & Performance
Job Description
Responsibilities
- Define the multi-year vision and roadmap for Ads Delivery & Performance across all Pinterest surfaces, aligning to company and Ads org priorities.
- Establish clear north-star outcomes (e.g., long-term revenue quality, retained advertiser value, Pinner experience) and the right cascade of input metrics and guardrails that drive them.
- Identify, define, and instrument the input metrics that most influence delivery and performance, such as supply health and coverage, eligibility and match rate, win rate, fill rate, pacing and spend smoothness, auction/bid competitiveness, latency/SLA adherence, ranking/model freshness and coverage, feature quality, CTR/CVR leading indicators, frequency and ad load distribution, quality/relevance signals, and policy/trust frictions.
- Build metric specs, ownership, alerting, and weekly business reviews so that teams operate to inputs (and understand their elasticities to outcomes like RPM, ROAS, advertiser retention, and Pinner satisfaction).
- Hire, develop, and inspire a diverse, high-performance organization of data science managers and senior ICs; set crisp role definitions, growth paths, and succession planning.
- Establish rigorous standards for experimentation, causal inference, and production analytics; ensure consistently trustworthy, decision-grade outputs.
- Work hand-in-hand with Product and Engineering on marketplace, ranking, pacing, and quality systems (e.g., auction design, supply/demand balancing, relevance, cold-start, ads load/frequency, shopping ads delivery).
- Translate ambiguous business questions into structured analyses and roadmaps; ensure the org moves from insight to shipped change with measurable impact.
- Own the measurement strategy for ads quality and delivery: counterfactual measurement, lift and incrementality, experiment design, long-term value, and guardrail monitoring for Pinner experience.
- Improve our decision frameworks, define minimum statistical standards, and drive consistency in A/B testing, quasi-experiments, and observational methods when experiments are impractical.
- Present complex analytical findings to executives and cross-functional partners with clarity; drive alignment on trade-offs across revenue, advertiser value, and Pinner experience.
- Create transparent operating cadences (QBRs/MBRs, input metric reviews, launch reviews) that keep teams focused on the levers that matter.
Qualifications
- MS or PhD in a quantitative field (Computer Science, Statistics, Math, Engineering, Economics or related) or equivalent practical experience.
- 10+ years in Data Science, Algorithmic Engineering, or Machine Learning, with significant impact in digital advertising or large-scale marketplaces.
- 5+ years directly managing data science organizations, including managing managers and scaling multi-team groups in a technology company.
- Deep experience with statistical analysis and causal inference, experiment design, and measurement for complex marketplaces.
- Deep experience with production analytics and large-scale data tooling (e.g., Python/R, SQL; Spark/Hive or similar).
- Deep experience with applied ML or relevance/ranking systems; familiarity with auction dynamics, pacing, bidding, and quality/relevance modeling.
- Proven track record of establishing and operating through input metrics tied to business outcomes.
- Excellent written and verbal communication; adept at influencing senior cross-functional partners and executives.
Locations
- Seattle, WA, US; Remote, US (Remote)
Salary
242,634 - 499,541 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
- Statistical analysis and causal inferenceintermediate
- Experiment design and measurement for complex marketplacesintermediate
- Production analyticsintermediate
- Large-scale data tooling (Python/R, SQL, Spark/Hive)intermediate
- Applied MLintermediate
- Relevance/ranking systemsintermediate
- Auction dynamicsintermediate
- Pacingintermediate
- Biddingintermediate
- Quality/relevance modelingintermediate
- Input metrics tied to business outcomesintermediate
- Written and verbal communicationintermediate
- Influencing senior cross-functional partners and executivesintermediate
Required Qualifications
- MS or PhD in a quantitative field (Computer Science, Statistics, Math, Engineering, Economics or related) or equivalent practical experience. (experience)
- 10+ years in Data Science, Algorithmic Engineering, or Machine Learning, with significant impact in digital advertising or large-scale marketplaces. (experience)
- 5+ years directly managing data science organizations, including managing managers and scaling multi-team groups in a technology company. (experience)
- Deep experience with statistical analysis and causal inference, experiment design, and measurement for complex marketplaces. (experience)
- Deep experience with production analytics and large-scale data tooling (e.g., Python/R, SQL; Spark/Hive or similar). (experience)
- Deep experience with applied ML or relevance/ranking systems; familiarity with auction dynamics, pacing, bidding, and quality/relevance modeling. (experience)
- Proven track record of establishing and operating through input metrics tied to business outcomes. (experience)
- Excellent written and verbal communication; adept at influencing senior cross-functional partners and executives. (experience)
Responsibilities
- Define the multi-year vision and roadmap for Ads Delivery & Performance across all Pinterest surfaces, aligning to company and Ads org priorities.
- Establish clear north-star outcomes (e.g., long-term revenue quality, retained advertiser value, Pinner experience) and the right cascade of input metrics and guardrails that drive them.
- Identify, define, and instrument the input metrics that most influence delivery and performance, such as supply health and coverage, eligibility and match rate, win rate, fill rate, pacing and spend smoothness, auction/bid competitiveness, latency/SLA adherence, ranking/model freshness and coverage, feature quality, CTR/CVR leading indicators, frequency and ad load distribution, quality/relevance signals, and policy/trust frictions.
- Build metric specs, ownership, alerting, and weekly business reviews so that teams operate to inputs (and understand their elasticities to outcomes like RPM, ROAS, advertiser retention, and Pinner satisfaction).
- Hire, develop, and inspire a diverse, high-performance organization of data science managers and senior ICs; set crisp role definitions, growth paths, and succession planning.
- Establish rigorous standards for experimentation, causal inference, and production analytics; ensure consistently trustworthy, decision-grade outputs.
- Work hand-in-hand with Product and Engineering on marketplace, ranking, pacing, and quality systems (e.g., auction design, supply/demand balancing, relevance, cold-start, ads load/frequency, shopping ads delivery).
- Translate ambiguous business questions into structured analyses and roadmaps; ensure the org moves from insight to shipped change with measurable impact.
- Own the measurement strategy for ads quality and delivery: counterfactual measurement, lift and incrementality, experiment design, long-term value, and guardrail monitoring for Pinner experience.
- Improve our decision frameworks, define minimum statistical standards, and drive consistency in A/B testing, quasi-experiments, and observational methods when experiments are impractical.
- Present complex analytical findings to executives and cross-functional partners with clarity; drive alignment on trade-offs across revenue, advertiser value, and Pinner experience.
- Create transparent operating cadences (QBRs/MBRs, input metric reviews, launch reviews) that keep teams focused on the levers that matter.
Tags & Categories