Data Science

Pinterest Labs tackles the most challenging problems in Machine Learning and Artificial Intelligence
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Data Science at Pinterest

To meet the changing needs of the 175+ million people who use Pinterest, we have to know both how they're using Pinterest today, and how they'll be using it in the future.

To do this, our data science team has created a systematic approach to data science, which gives us trustworthy conclusions that are both reproducible and automatable. We built tools that democratize the data, so engineers, product managers and designers can easily visualize and explore trends in content and experimental results. We also track how the world is changing as people modify their behaviors, diets, health routines, homes and interests. This gives us the knowledge we need to develop the revolutionary data-driven products our Pinners will be looking for next.

Meet the team

Pinterest’s data scientists have backgrounds in applied mathematics, computational biology, electrical engineering, physics, and software engineering.

Dan Frankowski
Shuo Xiang
Grace Huang

Publications
  • 1. Understanding Online Collection Growth Over Time: A Case Study of Pinterest

    Team: Caroline Lo, Justin Cheng, Jure Leskovec
    When: ACM International Conference on World Wide Web (WWW), 2017.
  • 2. Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior

    Team: Justin Cheng, Caroline Lo, Jure Leskovec
    When: ACM International Conference on World Wide Web (WWW), 2017.
  • 3. Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest

    Team: Caroline Lo, Dan Frankowski, Jure Leskovec
    When: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
  • 4. Trends on Pinterest

    When: USA Today, 2016

Blog