At a Glance
- Tasks: Lead innovative machine learning projects and solve real-world optimisation challenges.
- Company: Join Yelp, a forward-thinking tech company with a collaborative culture.
- Benefits: Enjoy competitive salary, flexible hours, and generous holiday allowance.
- Other info: Dynamic work environment with opportunities for career growth and fun team events.
- Why this job: Make a significant impact while working on cutting-edge ML technologies.
- Qualifications: Experience in machine learning, data analysis, and software engineering.
The predicted salary is between 70000 - 90000 £ per year.
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 United Kingdom. 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 a 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.
- £81 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.
- £81 per month to use toward qualifying wellness expenses.
- Quarterly team offsites.
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.
Senior Applied Scientist - Lead Optimization (Remote - UK) in Glasgow employer: Yelp, Inc
Yelp is an exceptional employer for those seeking a dynamic and collaborative work environment, particularly for the Senior Applied Scientist role. With a strong emphasis on individual authenticity and creative problem-solving, employees enjoy full project ownership from day one, competitive benefits including flexible working hours, and ample opportunities for professional growth through regular learning initiatives and team offsites. Located in the UK, Yelp fosters a culture of support and innovation, making it an ideal place for passionate machine learning scientists to thrive.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Applied Scientist - Lead Optimization (Remote - UK) in Glasgow
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We think you need these skills to ace Senior Applied Scientist - Lead Optimization (Remote - UK) in Glasgow
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Yelp, Inc. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Yelp, Inc
✨Brush Up on Your Statistics
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✨Get Comfortable with Python and R
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.