# Data Scientist II, Experimentation

**Company:** [Pinterest](https://hotfix.jobs/companies/pinterest)
**Location:** Remote
**Role:** Data Science
**Salary:** $114k – $235k/yr
**Experience:** 2+ years
**Skills:** Experimentation, Statistical Analysis, Experimental Design, Python, Git, Apache Airflow, Data Pipelines, Machine Learning Workflows, Scalable Code, Workflow Management
**Posted:** 2026-05-18

> Data Scientist focused on improving Pinterest's experimentation platform through advanced methodologies, process optimization, and cross-team collaboration. Requires strong stats background and 2+ years experience or PhD.

## Job Description

## What you’ll do
- Comb through the literature in experimentation to identify potential methodologies that can improve parts of our platform where we have the biggest opportunities.
- Make the process of setting up, running and evaluating experiments smoother and more repeatable for our platform users, ensuring that decisions are risk-aware and consistent.
- Write workflows to make our vast experimentation meta-data able to be leveraged by our team and outside of our team to better understand the experimentation landscape.
- Consult with product data science teams to debug, design or improve their experiments and experimentation process.

## What we’re looking for
- PhD in a relevant field (stats, applied math, biostatistics, etc…) OR 2+ years of hands-on experience working as a data scientist or applied scientist.
- Experience working directly on experimentation problems and an awareness of state of the art methodologies.
- The ability to write clean, efficient, and scalable code that can be easily maintained and extended by other team members.
- Proficiency in software development best practices, including version control systems such as Git, to ensure efficient collaboration, code management, and reproducibility in a data science environment.
- Familiarity with workflow management tools such as Apache Airflow to create and schedule data pipelines, allowing for automated and reliable execution of machine learning workflows.

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