Sr. Data Scientist, Trust and Safety

What you'll do

• Design and develop ML-assisted sampling techniques, applying expertise in statistical methods to accurately measure the prevalence of unsafe content, treating complex multi-component interactions as distinct measurement units.
• Apply rigorous statistical methods, drawing on knowledge of all kinds of sampling methods and their proper statistical application for complicated use cases, to calculate prevalence rates for specific Trust & Safety policy violations (e.g., Adult content, Self-harm, Harassment, Misinformation) and to further expand and improve the prevalence measurement.
• Build large-scale data pipelines to aggregate Pinner-generated queries, system responses, and recommended Pin images into a unified format for human and ML-based safety labeling.
• Partner cross-functionally to orchestrate "Offline" dashboards and robust "Online" production workflows for continuous safety monitoring.
• Collaborate closely with Trust & Safety teams to translate written safety policies into unified LLM prompts, coordinate BPO labeling queues, and calibrate labeler decision quality.

What we're looking for

• 5+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data.
• Strong interest and hands-on experience in platform safety, prevalence measurement, adversarial testing, responsible data measurement, or Trust & Safety.
• Deep familiarity with the measurement challenges of a complex ecosystem, including statistical interpretation of data.
• Experience designing and calibrating measurement frameworks, managing complex logging tables (e.g., user/interaction/component data), and defining directional success metrics.
• Strong quantitative programming (Python) and data manipulation skills (SQL/Spark); experience with complex ML pipelines and up-sampling.
• Ability to drive ambiguous measurement projects end-to-end, overcoming unstructured policy dependencies with high ownership.
• Excellent written and verbal communication skills, with the ability to advocate for decision quality before releasing metrics to executive leadership.