**About the Role**

The Delivery Logistics team builds real-world technology that powers our global marketplace. Our team of scientists works on diverse problems, including building real-time backend matching systems, creating new delightful user experiences, and serving predictions used for decision making across the marketplace.

In this role, you will use your quantitative skills to generate insights, develop new models, design metrics and run experiments. Uber operates in a constantly-evolving and competitive environment, and our success requires a deep understanding of our marketplace and the ability to build products that scale on a global footprint. You will work on high visibility, high impact projects which have significant impact on Uber's P+L.

**What You Will Do**

1. Solve ambiguous, challenging business problems using data-driven approaches
2. Work closely with multi-functional leads to develop a technical vision and drive team direction
3. Develop new methodologies for data science including modeling, coding, analytics, optimization, and experimentation
4. Present findings to business leaders to inform decisions
5. Design product experiments and interpret the results to draw detailed and impactful conclusions

\-\-\-\- Basic Qualifications ----

1. Completing or recently completed a Ph.D. in Statistics, Economics, Computer Science, Machine Learning, Operations Research, or another quantitative field
2. Knowledge of underlying mathematical foundations of optimization, stochastic processes, statistics, and machine learning
3. Strong problem solving and analytical abilities
4. Familiarity with SQL
5. Familiarity with a programming language such as R and Python

\-\-\-\- Preferred Qualifications ----

1. Ability to communicate effectively with both technical and business partners
2. Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation
3. Independence, excellent communication, and outstanding follow-through - you energetically tackle your work and love the responsibility of being individually empowered
4. Experience with exploratory data analysis, statistical analysis and model development
5. Experience in one of the following areas: Operations Management, Revenue Management and Pricing, Advertising, Experimental Design, Assortment Planning, Transportation
6. Familiarity with SQL
7. Proficiency with a programming language such as R, and Python
8. Previous industry experience (e.g., internships)

For New York, NY-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 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](https://www.uber.com/careers/benefits).

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 fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity 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](https://forms.gle/aDWTk9k6xtMU25Y5A).

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.