Sr. Data Scientist, Trust and Safety

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.

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At Pinterest, AI isnt just a feature, its a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .

Pinterest is the worlds 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 Pinterests 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 youll 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, Misinf