Pinterest is the world's leading visual search and discovery platform, serving over 500 million monthly active users globally on their journey from inspiration to action. As we scale experiences in a complicated ecosystem, ensuring they are safe, fair, and trustworthy is paramount. We are looking for a Senior Data Scientist to help lead Pinterest's Trust and Safety mandate by designing the foundations for measuring the prevalence of unsafe content across the platform.

In this role, you will design and build sampling frameworks, complex data aggregations, and measurement methodologies to track Trust & Safety policy violations across complex, multi-component user interactions. You will work in a highly collaborative and cross-functional environment, partnering with ML Engineers, Trust & Safety Ops, subject matter expert teams, and Product Managers. The results of your work will directly influence platform safety metrics, policy compliance, and executive-level visibility into platform health.

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.

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/quarter and therefore can be situated anywhere in the country.

Relocation Statement

• This position is not eligible for relocation assistance.

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