Applied Scientist

4 weeks ago


Remote, Canada Yelp Full time

**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.

As an applied scientist within the Ads group, you will be joining a team that focuses on pricing and auction bidding strategies, control systems, and experimentation. 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. Making sense of this data, thinking deeply about system design, and figuring out different network interactions is hard work, but these insights are hugely impactful to Yelp’s business.

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.

**What you'll do**:

- 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 you'll get**:

- This opportunity has the option to be fully remote in all locations across Canada.
- You can find more information about Yelp's five star benefits here

**What it takes to succeed**:

- A Bachelor’s Degree or an equivalent work experience is required.
- A degree in a quantitative discipline such as Computer Science, Statistics, Econometrics, Applied Math, Physics, etc.
- 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 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 applied 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.

**Closing**:

- 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, disability, or any other protected status._- 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._

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