Sr. Data Scientist, Trust and Safety

About Pinterest

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work.

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.

Qualifications

• 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.
• Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.
• 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.