Data Scientist II, Experimentation

We are seeking a data scientist with a strong background in experimentation and statistical analysis to help us improve and iterate on our experimentation platform. The successful candidate will play a key role in improving our experiment processes at scale, leveraging their expertise to drive innovation and help make sure that Pinterest users are receiving the most thoroughly data-driven features. With thousands of experiments running concurrently, the magnitude of our operations presents a significant opportunity for impact. If you possess a strategic mindset, proven experience in experimental design and analysis, and a passion for driving results, we invite you to join us in shaping the future of experimentation.

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.

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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