SUMMARY

Yelp engineering culture is driven by our values: we’re a cooperative team that values individual authenticity and encourages creative solutions to problems. All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams. At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment.
Are you passionate about applying experimentation, causal inference, analysis, and machine learning models to make high-quality and high-impact decisions? Do you want to help local businesses grow and succeed? Yelp’s Ad Bid Optimization team works on challenging, multi-disciplinary problems at scale—spanning economics, machine learning, statistics, and engineering—with the goal of creating a valued marketplace between customers and advertisers.

As an Applied Scientist at Yelp, you will contribute to our marketplace for customers and advertisers by applying your knowledge of experimental and observational analysis alongside modeling and mechanism design. The range of problems you may take on here at Yelp include:

• How should we design our ad systems with the right incentives to promote a valued marketplace?
• How should we improve our dynamic auction system for advertisers?
• What causal mechanisms underpin our advertising systems?
• How can we best design an experiment to measure complex network effects?

If this all sounds exciting to you, come join our team!
This opportunity is fully remote and does not require you to be located in any particular area in Canada. We welcome applicants from throughout Canada. 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.

• Identify and own complex problems, form testable hypotheses and drive significant business impact.
• Conduct end-to-end analyses, from wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas.
• Communicate your conclusions to technical and non-technical audiences alike.
• Develop and deploy ML models into production and continuously improve their performance.
• Keep the team and our projects current on new developments in experimentation and modeling.
• Mentor Applied Scientists and share the skills you’ve learned