Staff Data Scientist, Engagement Ecosystem
Confirmed live in the last 24 hours
Job Description
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. Creating a career you love? It’s Possible.
At Pinterest, AI isn't just a feature, it's 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.
We are looking for a Staff Data Scientist for our Engagement Ecosystem. You will shape the future of people-facing and business-facing products we build at Pinterest. Your expertise in quantitative modeling, experimentation and algorithms will be utilized to solve some of the most complex engineering challenges at the company. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Design, Research, Product Analytics, Data Engineering and others. The results of your work will influence and uplevel our product development teams while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners, creators, advertisers and merchants around the world.
What you’ll do
- Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks.
- Lead projects on:
- Tradeoffs between organic engagement and advertising.
- Deep dives on how engagement metrics impact monetization and retention.
- Understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes.
- Designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries.
- Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
- Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
- Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.
- Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.
- Use advanced ML, causal inference, and generative AI techniques to model and explain complex interactions across the Pinterest ecosystem (e.g., organic engagement, ads, content quality, monetization), turning highly ambiguous business questions into clear, testable hypotheses and decision frameworks for Core and Monetization leadership.
- Apply AI-assisted analysis and developer tools (e.g., copilot, intelligent dashboards) to accelerate exploration, improve code quality, and scale insight generation, ensuring we can quickly evaluate tradeoffs like ad load vs. Pinner engagement.
- Champion responsible and scientifically rigorous use of AI across the ecosystem, setting best practices for instrumentation, experimentation, and metric design so that AI-driven changes balance engagement, revenue, and long-term user trust.
What we’re looking for
- 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous p
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