Advance Shopping at Pinterest by developing methods and models to explain why certain content is being promoted (or not) for a Pinner, influencing development teams and driving product innovation.
Requirements
7+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data.
Strong interest and experience in recommendation systems and causal inference.
Strong quantitative programming (Python/R) and data manipulation skills (SQL/Spark).
Responsibilities
Develop methods and models to explain why certain content is being promoted (or not) for a Pinner.
Develop robust frameworks, combining online and offline methods, to comprehensively understand the outputs of our recommendations.
Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners.
Ensure that our recommendation systems produce trustworthy, high-quality outputs to maximize our Pinner’s shopping experience.
Work cross-functionally to build relationships, proactively communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest.
Relentlessly focus on impact, whether through influencing product strategy, advancing our north star metrics, or improving a critical process.
Mentor and up-level junior data scientists on the team.
Other
Ability to work independently and drive your own projects.
Excellent written and communication skills, and able to explain learnings to both technical and non-technical partners.
A team player eager to partner with cross-functional partners to quickly turn insights into actions.
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.
This position is not eligible for relocation assistance.
