About the position This is a pivotal Director-level leadership role with the opportunity to strengthen the foundations of trust on Pinterest while advancing how we understand Pinners and content at scale. You will lead a high-impact data science organization and partner deeply with Product, Engineering, and Policy to build and operate the next generation of integrity, user understanding, and content understanding systems that power enforcement and distribution decisions across the platform.The core mandate is to evolve the organization from reactive moderation and lagging harm reporting into a proactive, metrics-driven operating system—where we manage leading indicators that predict risk, improve understanding quality, and keep Pinterest safe, inspiring, and useful. What you’ll do: Set Strategy & Roadmap : Define the multi-year vision and priorities across Trust & Safety, User Understanding, and Content Understanding—establishing north-star outcomes (e.g., prevalence & reach reduction, user trust, ecosystem health, content quality, user and content understanding) and the leading indicators that drive them. Operationalize Leading Indicators : Identify, instrument, and build operating cadences around key input metrics that influence integrity and understanding—such as policy risk signals, spam/abuse rates, account authenticity, coverage and latency, precision/recall of enforcement, appeal overturn rates, content quality signals, and user feedback/friction signals. Lead a Talented Organization : Hire, develop, and inspire a world-class team of data science managers and senior ICs spanning product analytics, ML evaluation, experimentation, causal inference, and integrity measurement. Partner to Ship Impact : Work hand-in-hand with Product, Engineering and Policy teams to deliver end-to-end improvements across detection, enforcement precision, and distribution systems—translating ambiguous trust and understanding questions into shipped product/ML capabilities with measurable impact. Elevate Measurement & Decision Quality : Own measurement strategy for safety, integrity, and understanding quality, employing causal inference, experimentation, offline evaluation, and long-term value measurement to quantify trade-offs and reduce unintended consequences. Communicate and Align Across Stakeholders : Present complex analyses and recommendations to executives and cross-functional partners, driving alignment on difficult trade-offs between safety, expression, growth, and product usability.

Responsibilities

• Set Strategy & Roadmap : Define the multi-year vision and priorities across Trust & Safety, User Understanding, and Content Understanding—establishing north-star outcomes (e.g., prevalence & reach reduction, user trust, ecosystem health, content quality, user and content understanding) and the leading indicators that drive them.
• Operationalize Leading Indicators : Identify, instrument, and build operating cadences around key input metrics that influence integrity and understanding—such as policy risk signals, spam/abuse rates, account authenticity, coverage and latency, precision/recall of enforcement, appeal overturn rates, content quality signals, and user feedback/friction signals.
• Lead a Talented Organization : Hire, develop, and inspire a world-class team of data science managers and senior ICs spanning product analytics, ML evaluation, experimentation, causal inference, and integrity measurement.
• Partner to Ship Impact : Work hand-in-hand with Product, Engineering and Policy teams to deliver end-to-end improvements across detection, enforcement precision, and distribution systems—translating ambiguous trust and understanding questions into shipped product/ML capabilities with measurable impact.
• Elevate Measurement & Decision Quality : Own measurement strategy for safety, integrity, and understanding quality, employing causal inference, experimentation, offline evaluation, and long-term value measurement to quantify trade-offs and reduce unintended consequences.
• Communicate and Align Across Stakeholders : Present complex analyses and recommendations to executives and cross-functional partners, driving alignment on difficult trade-offs between safety, expression, growth, and product usability.

Requirements

• MS or PhD in a quantitative field or equivalent experience.
• 10+ years in Data Science, Algorithmic Engineering, or ML, with significant impact in digital advertising or large-scale marketplaces.
• 5+ years of managing data science organizations, including managing managers.
• Deep experience with: Statistical analysis, causal inference, and experiment design for complex marketplaces.
• Production analytics and large-scale data tooling (Python/R, SQL, Spark/Hive).
• Applied ML or relevance/ranking systems; familiarity with auction dynamics, pacing, and quality modeling.
• Proven ability to operate through input metrics tied to business outcomes.
• Excellent communication and influencing skills.