About the Team
The Uber Maps team builds and improves Uber’s geospatial technologies, which form the crucial infrastructure for Uber services. We work across a diverse array of problem domains, including: curating location (i.e. ‘Places’) data, building location search functionality for setting destinations for each Uber trip, crafting base maps and detecting map errors, and optimizing routes and travel time predictions. These services power every marketplace decision through dispatch and pricing.
If you’re passionate about driving innovation and have a strong background in the mentioned areas, we encourage you to join our team and help craft the future of Uber’s critical maps technologies.
You can learn more about our team through:
- Fixing Map Errors with GPS Data
- What’s my ETA? A Billion $ question
- Rethinking GPS: Engineering Next-Gen Location at Uber
- Enhancing the Quality of Uber’s Maps with Metrics Computation
About the Role
We are hiring applied scientists who are passionate about solving challenging problems at scale to help improve Uber’s geospatial capabilities. We’re seeking individuals with proven foundations in machine learning, statistics, experimental design, and/or operations research.
As an applied scientist on the team, we seek to tap into your expertise in various ways, including but not limited to:
- Solving ambiguous problems through Statistical / ML / Optimization modeling
- Partnering with product development through prototyping and productionizing models, building data observability and metrics, and experimentation via close collaboration with engineering and product teams
- Driving team roadmap and vision through finding opportunities and building data-driven solutions.
- Delivering insightful presentations on models, solutions, and data analytics findings to partners and leadership.
- Bachelor’s degree in Statistics, Machine Learning, Operations Research, or other quantitative fields.
- 2+ years of industry experiences an Applied Scientist or equivalent (1+ if holding an advanced degree in a related quantitative field)
- Experience with exploratory data analysis, statistical analysis and testing, causal analysis and ML model development.
- Experience in experimental design and analysis.
- Experience using Python at scale with large data sets.
- Experience with tools like SQL, R, and Spark in a production environment
- Experience working in a marketplace environment within the technology industry
- Advanced Degree in Statistics, Machine Learning, Operations Research, or other quantitative field
- Strong communication skills through documentation and presentations.
- Experience partnering with cross-functional stakeholders to implement decisions.
- Experience in algorithm development and prototyping.
- Experience with building and improving products through metrics design, in-depth analysis, model prototyping and development, etc
- Experience with building data schemas and scalable ETL pipelines for metrics and dashboards
For San Francisco, CA-based roles: The base salary range for this role is $149,000 per year - $165,500 per year.
You will be eligible to participate in Uber’s bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.
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.