Uber Eats Jobs

Staff Scientist

• Competitive Intelligence

The Global Intelligence team is at the forefront of shaping Uber's competitive strategy through data-driven insights. We leverage external and internal data to evaluate Uber's performance within a rapidly evolving landscape of competitors, market dynamics, and product innovations. Understanding success in this complex environment requires rigorous analytical frameworks, innovative methodologies, and scalable data solutions.

We are seeking an experienced candidate with a passion for tackling highly ambiguous, high-impact problems using data.

You will tackle highly ambiguous, high-impact problems, applying your expertise in statistical modeling, machine learning, causal inference, and economics to uncover insights that shape critical business and product decisions. Beyond analysis, you will architect scalable data products, mentor other scientists, and collaborate cross-functionally to embed competitive intelligence into Uber's decision-making at the highest levels.

What the Candidate Will Do

Leverage advanced analytical methods including statistical modeling, causal inference, funnel analysis, and deep-dive analytics to uncover Uber's largest opportunities in a competitive landscape and drive high-impact business decisions.

Translate complex analyses into actionable insights and present findings to business leaders, executives, and cross-functional partners to shape strategy.

Collaborate with Product, Operations, and Engineering teams to define and execute a roadmap of high-impact initiatives, ensuring competitive intelligence is deeply integrated into Uber's decision-making.

Provide technical mentorship and thought leadership, elevating the team's analytical rigor and advancing best practices in statistical and ML methodologies.

Basic Qualifications

Ph.D., M.S., or Bachelor's degree in Economics, Statistics, Machine Learning, Operations Research, or a related quantitative field.

6

• years of industry experience as an Applied Scientist, Data Scientist, or equivalent role.
• Strong foundation in statistical methods and modeling, including experimental design (e.g., A/B testing) and causal inference.

Proficiency in SQL, along with experience coding in Python or R for data analysis and modeling.

Preferred Qualifications

Advanced expertise in experimental design and causal inference, including observational studies and quasi-experimental methods.

Hands-on experience working with messy, incomplete, and noisy data to extract insights and drive business impact.

For San Francisco, CA-based roles: The base salary range for this role is USD$216,000 per year

For Seattle, WA-based roles: The base salary range for this role is USD$216,000 per year

For Sunnyvale, CA-based roles: The base salary range for this role is USD$216,000 per year

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of compensation.

You will also be eligible for various benefits. More details can be found at the following link.

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.

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.