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# Sr. Data Scientist, Monetization

- **Company:** Pinterest
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $164,428.0 - $287,749.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Apache Hive, Python (Programming Language), SQL Databases
- **Published:** July 27, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/7402537?backUrl=%2Fcareer%2F7402537%2FSr-Data-Scientist-Monetization-California-San-Francisco

## About the Role

* Bachelor's/Master's degree in a relevant field such as Data Science, or equivalent experience
 * 5+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems on web-scale data.
 * Strong fundamentals in statistics, experimentation.
 * Domain knowledge in ecosystem, ads, or real-time-bidding.
 * Proven ability to apply scientific methods to solve real-world problems on web-scale data.
 * Proficiency in SQL/Hive/Python.

## Description

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 analysisto 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 may span from collecting entirely new datasets to building dashboards to track components of a metric (e.g., monitoring conversion data for missing values, implausible values, duplicated data, etc. by advertiser over time).
 * Leadership: Lead and mentor the scope of work for data scientists in the same area, demonstrating high-quality output of both yourself and others for whom you are responsible. Provide continuous and candid feedback, recognizing individual strengths and contributions and flagging opportunities to improve performance.


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