Data Scientist II, Experimentation

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.

Information regarding the culture at Pinterest and benefits available for this position can be found

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.