Are you intrigued by data? Yelp has hundreds of millions of pieces of user-contributed content, millions of users and business listings, and hundreds of thousands of advertising customers – and all of these numbers are constantly growing. Making sense of this data, deducing relationships between variables, and figuring out different interactions is hard work, but these insights are hugely impactful to Yelp’s business. Applied scientists uncover these insights through exploratory research and analysis, and carry the ideas all the way through to production-grade statistical or predictive models. They work in areas including pricing models and auction bidding strategies, learning to rank applications, personalized recommender systems, trust and safety / spam detection, and causal inference.

Yelp engineering culture is driven by our values: we’re a cooperative team that encourages individual authenticity and “unboring” solutions to problems. We enable all new team members to deploy working code their first week, and your impact will only grow from there with the support of your manager, mentor, and team. At the end of the day, we’re all about helping our users, growing as engineers and scientists, and having fun in a collaborative environment.

Sound exciting? Keep reading.

Where You Come In:

• Identify and own challenging problems, form testable hypotheses, and drive significant business impact
• Lead the design and analysis of experiments or development of causal and predictive models to test your ideas
• Collaborate with product and engineering to affect changes in production systems and provide intelligence to other teams
• Communicate your conclusions to technical and non-technical audiences alike
• Keep the team and our projects current on new developments in ML and statistics by reading papers and attending conferences and local events
• Productionize and automate model pipelines within Python services

What it Takes to Succeed:

• Experience with data analysis/statistical software and packages (pandas/statsmodels/sklearn within Python, R, etc.)
• Experience with predictive modeling/machine learning, forecasting, or causal inference
• A degree in a quantitative discipline such as Computer Science, Statistics, Econometrics, Applied Math, etc.
• A love for writing beautiful code; you don’t need to be an expert, but experience is a plus and we’ll expect you to learn on the job
• A demonstrated capability for original research, the curiosity to uncover promising solutions to new problems, and the persistence to carry your ideas through to an end goal
• The motivation to develop deep product and business knowledge and to connect abstract modeling and analysis tasks with business value
• Comfortable working in a Unix environment

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