About the Role
We are looking for a Staff Scientist (L5B) with deep expertise in Operations Research, optimization, and algorithmic decision-making to serve as a technical leader for the team.
This role will focus on designing and implementing optimization frameworks and scalable decision algorithms to improve key marketplace outcomes such as matching efficiency, driver utilization and rider wait times.
The ideal candidate combines strong mathematical optimization expertise with practical experience building scalable decision systems in large-scale online platforms.
You will partner closely with Engineering, Product, and other Science teams to develop and deploy optimization-based solutions that power critical marketplace decisions in real time.
What the Candidate Will Need / Bonus Points
---- What the Candidate Will Do ----
As a Staff Scientist, you will be responsible for influencing product strategy decisions to drive significant impact across key charters on the team & for raising the bar on technical rigor in the team.
Develop optimization models for Marketplace Problem
- Identify opportunities where optimization techniques can improve marketplace balance, driver utilization, and rider experience.
- Design optimization models to solve complex marketplace problems such as dispatch optimization, driver repositioning, and pricing optimization.
- Translate business problems into mathematical optimization formulations (e.g., linear programming, mixed-integer programming, network flow models etc.).
Build Scalable decision systems
- Develop scalable optimization algorithms capable of operating in large-scale, real-time environments.
- Work closely with Engineering to productionize optimization models and ensure they integrate effectively with Uber’s systems.
Technical Leadership & Cross-functional collaboration
- Build & lead the execution of the technical roadmap for the charters
- Mentor scientists on optimization modeling, algorithm design, and decision science methodologies.
- Establish best practices for translating theoretical optimization methods into production-ready systems.
- Work closely with Product and Engineering leadership to identify high-impact optimization opportunities.
- Communicate algorithmic approaches and model trade-offs clearly to technical and non-technical stakeholders.
---- Basic Qualifications ----
- Undergraduate or graduate degree in Operations Research, Applied Mathematics, Computer Science, or related quantitative field.
- 9+ years of industry experience applying optimization, operations research, or algorithmic decision science in real-world systems.
- Strong experience developing and implementing optimization models (LP, MIP, stochastic optimization, etc.).
- Proficiency in Python and SQL, and familiarity with optimization tools and libraries
- Experience collaborating with Engineering teams to deploy algorithms in production systems.
---- Preferred Qualifications ----
- Master’s/Ph.D. Degree in Operations Research, Applied Mathematics, Computer Science, or related quantitative field.
- Experience designing optimization solutions for large-scale online platforms or marketplaces.
- Experience with real-time decision systems.
- Demonstrated ability to mentor scientists.
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let’s move it forward, together.
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.
*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.