Quick summary
- Work type
- Remote
- Location
- San Francisco, CACalifornia
- Salary
- $139,764–$287,749 / yr
- Posted
- 14 days ago
- Nearby
- 99+ roles within 25 mi
Market check
Salary context
How this pay compares to similar roles
This role pays more than 87% of similar roles. Most pay $126,800–$190,600 — the shaded band above. At the midpoint, this role pays about $214k versus about $159k for comparable roles.
Based on 240 similar postings.
Employer
About Pinterest
Pinterest is a visual discovery and inspiration platform where people find ideas for home, style, recipes, and more. It serves hundreds of millions of users worldwide through its image and video pinboard product.
Pinterest currently has 37 open roles on FindRole.
Listed pay typically runs $164,695–$332,012 across 37 roles with salary data.
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At a glance
TL;DR · Sr. Data Scientist, Trust and Safety
As a Senior Data Scientist on Pinterest’s Trust and Safety team, you will design and implement sampling frameworks and data aggregations to measure the prevalence of unsafe content across the platform. Your day-to-day involves developing ML-assisted sampling techniques, building large-scale data pipelines for safety labeling, and creating robust dashboards for continuous monitoring. You will collaborate closely with cross-functional teams including ML Engineers, Trust & Safety Ops, and Product Managers to ensure policy compliance and executive-level visibility into platform health. The role requires 5+ years of experience in analyzing web-scale data, expertise in statistical methods, and hands-on knowledge of platform safety and prevalence measurement. Strong skills in Python, SQL/Spark, and complex ML pipelines are essential, along with the ability to drive ambiguous projects end-to-end and communicate effectively at all levels.
Skills
What you'll do
- Design and develop ML-assisted sampling techniques for measuring unsafe content prevalence.
- Apply rigorous statistical methods to calculate prevalence rates for Trust & Safety policy violations.
- Build large-scale data pipelines to aggregate user-generated queries and system responses for safety labeling.
- Partner with cross-functional teams to create offline dashboards and online production workflows for continuous monitoring.
- Translate written safety policies into unified LLM prompts and coordinate BPO labeling queues.
What we're looking for
- 5+ years of experience in data analysis and applying scientific methods to solve real-world problems.
- Expertise in designing sampling techniques and statistical methods for measuring unsafe content prevalence.
- Proficiency in building large-scale data pipelines and aggregating complex user interactions for safety labeling.
- Experience in cross-functional collaboration, including working with ML Engineers and Trust & Safety teams.
- Strong quantitative programming skills in Python, SQL, Spark, and experience with complex ML pipelines.
- Ability to drive ambiguous measurement projects end-to-end and advocate for decision quality at executive levels.
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