Data Scientist Yelp
At Yelp, we see our 42M+ 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!
Minimum BA/BS degree in Statistics, Math, Physics, Computer Science or related quantitative degree
MS/PhD preferred, or 2+ years of relevant experience
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 R, Matlab, or SAS
Fluency with at least one scripting language such as Python
Familiarity with relational databases and SQL
Comfortable working in a Unix environment
Excellent typing ability in the presence of flying nerf darts
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.
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