Job Description
Staff Data Scientist, Content
- San Francisco, California, United States / Remote, United States
- Engineering
- Regular
About Pinterest:
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.
We are looking for a Staff Data Scientist for the Content org. You will shape the future of people-facing and business-facing products we build at Pinterest. Your expertise in quantitative modeling, experimentation and algorithms will be utilized to solve some of the most complex engineering challenges at the company. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Design, Research, Product Analytics, Data Engineering and others. The results of your work will influence and uplevel our product development teams while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners, creators, advertisers and merchants around the world.
What you’ll do:
- Ecosystem analysis to develop a framework to evaluate the health of Pinterest content corpus, such as where do we have content gaps; what is the value of fresh content; what is the value of diverse content.
- Experimentation to Design and evolve our experimentation capabilities and tools to evaluate the changes we make to our content levers. Advise on experimentation best practices; identifying flaws in experiment practices and results; building tools for experiment analysis.
- Opportunity sizing and analysis to identify what are the biggest content levers to help drive user engagement and retention.
- Product recommendations: Clearly communicate recommendations to product and engineering leadership on how we can evolve our internal strategy to address shortcomings observed through deep analysis.
- Creating and tracking success metrics. Identify the right measures of success for product teams and help them track those metrics. Own the full lifecycle of those metrics from logging requirements, metrics definition, prototype pipelines, and improvements.
- Lead and mentor the scope of work for at least one other individual, demonstrating high-quality output of both yourself and others for whom you are responsible. Provide continuous and candid feedback, recognizing individual strengths and contributions and flagging opportunities to improve performance.
What we’re looking for:
- 8+ 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.
- Extensive experience solving analytical problems using quantitative approaches including in the fields of Statistics, Machine Learning, Forecasting, Econometrics or other related fields.
- Familiarity with online experiment and its pitfalls.
- Statistical rigor. Can guide the team and others on statistically valid approaches to problems.
- A scientifically rigorous approach to analysis and data, and a well-tuned sense of skepticism, attention to detail and commitment to high-quality, results-oriented output.
- Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R).
- Excellent communication skills and ability to explain learnings to both technical and non-technical partners.
- A team player who’s able to partner with cross-functional leadership to quickly turn insights into actions.