Sr. Data Scientist, Monetization

San Francisco, CA, US; Seattle, WA, USOn-site 3w ago

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 Data Scientists to join our Monetization Engineering organization. You will take end to end ownership of designing, researching, building, and delivering data products as well as collaborate with XFNs to formulate, experiment, and evolve Pinterest’s strategy in the Ads space.

What you’ll do:

Deep strategic analysis to answer core questions such as: How do we assess the trade-off between metrics change? How should we evaluate overall impact from changes in one component of the ads ecosystem?
Opportunity sizing and analysis. Should Pinterest adjust programmatic ad load based on ? Write clear, actionable analyses that help teams identify areas of improvement and investment.
Modeling: Build segmentation models to assess supply to inform pricing strategy.
Improve decision velocity and quality using data scientist tool kit: experimentation, causal inference techniques, etc. Design measurement strategy, advise on experimentation best practices, identifying flaws in experiment practices and results; building tools for experiment analysis etc.
Creating and tracking success metrics. Identify the right measures of success for engineering teams and help them track those metrics. Break down high-level metrics into actionable segments. This work

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