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.
We’d love to have you apply, even if you don't feel you meet every single requirement in this posting. At Yelp, we’re looking for great people, not just those who simply check off all the boxes.
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
#LI-Remote
At Yelp, we believe that diversity is an expression of all the unique characteristics that make us human: race, age, sexual orientation, gender identity, religion, disability, and education — and those are just a few. We recognize that diverse backgrounds and perspectives strengthen our teams and our product. The foundation of our diversity efforts are closely tied to our core values, which include “Playing Well With Others” and “Authenticity.”
We’re proud to be an equal opportunity employer and consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition or disability.
We are committed to providing reasonable accommodations for individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, you may contact us at accommodations-recruiting@yelp.com or 415-969-8488.
Note: Yelp does not accept agency resumes. Please do not forward resumes to any recruiting alias or employee. Yelp is not responsible for any fees related to unsolicited resumes.