Data Scientist

At Yelp, we see our 100M+ reviews not only as a great source of information about the best salted caramel ice cream in SF, but also as a vast storehouse of data. You will help us use that data to connect our users with great local businesses. You will work tightly with the product and engineering team to answer questions ranging from who is more likely to like a flavor of ice cream to which experimental features move the needle... and you’ll help us define the needle. You’ll ask critical questions about our metrics and our experiments in order to wrest statistical meaning from the jaws of noise, and you’ll crunch the numbers on our existing features to help shape new products. In this role you’ll be tackling a variety of projects ranging from design and execution of A/B tests and user behavior analyses, to data-driven blog posts and product insights. As your role grows you’ll identify new analyses, scope new data-driven products and help us define new metrics and design new data driven features.

If you’re passionate about asking and answering questions in large datasets and you are able to communicate that passion to product and engineering teams, we want to hear from you!

We Are Looking For:

  • Minimum BA/BS degree in Statistics, Math, Economics, Physics or related quantitative degree.
  • 2+ years of relevant industry experience.
  • MS/PhD preferred.
  • Endless ideas about how to leverage Yelp's unique data set.
  • Extensive experience with analytical and quantitative problem solving.
  • Experience with analysis tool(s) such as pandas or R.
  • Fluency with at least one scripting language such as Python.
  • Familiarity with relational databases and SQL.
  • Comfortability working in a Unix environment .


  • Experience with MapReduce, Hadoop, Hive or similar systems.
  • Specific interest in search engines, recommendation systems, or social networks.
  • Active contributor to open source software.
  • Interested in applying? Sweet! Share with us why you want to work at Yelp and don't forget to mention any technical side projects, open source contributions, academic papers, and personal websites/blogs.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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