Staff Data Scientist

Description:

Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.

Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.

Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more.

Today, our Pinners explore recommendations from our inventory of billions of pins blended with paid content. But how can we best blend this content to ensure we’re optimizing for the long-term goals of both our customers and the business? This decision is informed by numerous factors that consider the Pinner’s intent, the quality of their experience, the quality of the content and the value provided to the company. As we innovate in our efforts to improve the whole page experience, we’re looking for an experienced data scientist to navigate the complex trade-offs, identify the opportunities, and pave the path forward to improve both the Pinner experience and our business performance.

What You’ll Do:
 

• Create a roster of high-impact analytical opportunities to steer the direction of our investments.
• Deep strategic analysis to understand opportunities for improvements in (e.g.) our utility functions, ranking algorithms, proxy metrics or success measures.
• Design experiments so that we can better understand causal relationships between factors such as quality, relevance, engagement, and ad load.
• Apply causal inference to model cause and effect and motivate further randomized experiments.
• Prototype and create tools that others can use to make decisions on online and offline experiments.
• Up-level data scientists across the organization through mentorship, partnership and constructive feedback.
• Build processes and solutions to improve the quality, rigor and agility of analyses.