About the Role
The Uber Maps team is at the forefront of advancing Uber’s geospatial technologies, crucial for driving the efficiency and reliability of Uber services. We work across a diverse array of problem domains, including: curating and improving precision of location (i.e. ‘Places’) data, developing cutting-edge location search algorithms for each Rides pickup/dropoff and Eats delivery, building base maps and correcting map errors, as well as optimizing routes and travel time predictions, and more. These pivotal technologies are the backbone of every decision made in our marketplace, influencing dispatch and pricing strategies directly.
If you’re passionate about driving innovation and have relevant experiences in tech or marketplace settings, we sincerely welcome you to join our team and shape the future of Uber’s geospatial technologies together.
You can learn more about our team through:
What You’ll Do
We are looking for data scientists who thrive in solving complex, large-scale challenges to advance Uber’s geospatial capabilities. Our ideal candidates are professionals with substantial hands-on experience from the tech industry, with solid knowledge in machine learning, statistics, experimental design, and/or operations research. Experiences with solving multi-sided marketplace problems is a plus.
In this role, you will apply your expertise to a variety of critical tasks, including
- Developing models to address ambiguous challenges using statistical, machine learning, and optimization techniques.
- Collaborating closely with product development teams to prototype and productionize models, enhance data observability and metrics, and conduct experiments.
- Driving team roadmap and vision through identifying opportunities and building data-driven solutions.
- Delivering insightful presentations on models, solutions, and data analytics findings to stakeholders and leadership.
Basic Qualifications
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Ph.D., M.S. or Bachelor’s degree in Statistics, Economics, Mathemathics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
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Coding and SQL proficiency and ability to develop statistical analysis and algorithm prototyping in Python or R.
- Design experiments and interpret the results to draw detailed and actionable conclusions across a variety of key performance indicators
Preferred Qualifications
- 4+ years of industry experience as an Applied or Data Scientist or equivalent.
- Experience working in a marketplace environment within the technology industry
- Proficiency in programming languages including Java and Go.
- Experience in algorithm prototyping and development.
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Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams.
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Experience leading key technical projects and substantially influencing the scope and output of others.
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Experience communicating qualitative research methods and findings to non-qualitative researchers
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Track record of engaging senior leadership effectively to build understanding of and consensus for the viewpoints of the team
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Solid theoretical and applied ML skills and a strong background in mathematics and stats
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
