Sr. Data Scientist, Trust and Safety

Pinterest

Remote Hybrid

Quick summary

Work type
Remote
Location
San Francisco, CACalifornia
Salary
$139,764–$287,749 / yr
Posted
14 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $159k
This role $214k
$77k most similar roles pay here $310k

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.

Most-posted roles

View all roles at Pinterest

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.

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.

More like this

Similar roles

Sr. Data Scientist, Core

Pinterest

Remote (San Francisco, CA) 14 days ago $139,764$287,749
Python SQL R PyTorch TensorFlow scikit-learn Machine Learning Statistical Modeling Forecasting Econometrics Data Engineering CI/CD
Remote

Data Scientist, Lead

Booz Allen Hamilton

Washington, District of Columbia 76 days ago $112,800$257,000
Python R SQL NoSQL Machine Learning AI NLP Docker CI/CD PostgreSQL AWS Kubernetes

Data Scientist Lead

PNC

Pittsburgh, PA +3 14 days ago $80,000$209,300
Python SQL R Apache Spark Hadoop TensorFlow Scikit-learn Keras Pandas Numpy Machine Learning Data Mining Data Science CI/CD Git Jupyter Notebook AWS Google Cloud Platform Azure

Data Scientist Sr

PNC

Pittsburgh, PA +3 14 days ago $65,000$186,940
Python SQL R Tableau PowerBI MachineLearning DataMining DataVisualization DataScience AWS GoogleCloud Azure CI/CD Git JupyterNotebook Pandas Scikit-learn TensorFlow Keras PostgreSQL MongoDB