Senior Applied Scientist - Lead Optimization

Summary

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.

At Yelp’s Lead Optimization team, we are passionate about connecting users with the service professional that matches their needs. Whether you’re looking for a plumber, contractor, or an electrician, we believe that better matching and accurate pricing in Yelp’s Service marketplace results in higher engagement from business owners and users, who are a critical part of what allows Yelp’s business model to thrive.

We are looking for an entrepreneurial, self-driven machine learning scientist who will help invent the future of optimization at Yelp. In this role, you’ll hone your skills in ML techniques like GBDT, ensemble models, and embeddings while building scalable industrial systems.

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.

This opportunity requires you to be located in the Republic of Ireland. 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 and 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
• Comfortable working in an Unix environment.
• Sufficient software engineering skills to effectively work with software engineers.
• 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.

What you'll get:

• Full responsibility for projects from day one, a collaborative team, and a dynamic work environment.
• Competitive salary, a pension scheme, and an optional employee stock purchase plan.
• 25 days paid holiday (rising to 29 with service), plus one floating holiday.
• €150 monthly reimbursement to help cover remote working expenses.
• €95 caregiver reimbursement to support dependent care for families.
• Private health insurance, including dental and vision.
• Flexible working hours and meeting-free Wednesdays.
• Regular 3-day Hackathons, bi-weekly learning groups, and productivity spending to support and encourage your career growth.
• Opportunities to participate in digital events and conferences.
• €95 per month to use toward qualifying wellness expenses.
• Quarterly team offsites.

Closing

Yelp values diversity. 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.