1. What is a Research Scientist at Uber?

As a Research Scientist at Uber—specifically within the Policy Economics and Applied Science domains—you are the analytical engine behind the company’s most critical strategic and public-facing decisions. You will conduct rigorous external research that forms the foundation for Uber’s policy positions, helping policymakers, thought leaders, and the general public understand the complex dynamics of the platform economy.

Beyond external influence, this role has a profound internal impact. You will serve as a vital bridge between policy experts and technical teams, conducting deep-dive internal research that supports executive leadership. Your insights will help weigh complex trade-offs across competitive, legal, operational, and product considerations. Whether you are analyzing driver and courier earnings, evaluating marketplace matching algorithms, or assessing the impact of new pricing models, your work directly shapes the future of mobility and delivery.

This role requires a unique blend of academic rigor, technical execution, and business acumen. You will not be conducting research in a vacuum; you will be answering fast-paced, high-profile policy questions while simultaneously driving multi-year research collaborations with external partners. Expect a highly visible, fast-paced environment where your econometric models and statistical analyses directly inform real-world strategies and global policy narratives.

2. Common Interview Questions

3. Getting Ready for Your Interviews

Preparing for the Research Scientist interview at Uber requires a strategic approach. You must demonstrate not only your technical depth but also your ability to translate complex economic concepts into actionable business and policy advice.

Econometric and Statistical Rigor – Uber relies heavily on causal inference and advanced statistical modeling to understand marketplace dynamics. Interviewers will evaluate your ability to design robust experiments, apply quasi-experimental methods to observational data, and identify causal relationships in complex, noisy environments. You can demonstrate strength here by clearly explaining the assumptions behind your models and how you handle violations of those assumptions.

Technical Execution and Scaling – A strong theoretical background is not enough; you must be able to execute your research independently. Interviewers will assess your proficiency in SQL, Python, or R to ensure you can extract, manipulate, and analyze massive datasets efficiently. Showcasing clean, scalable code and a practical approach to data wrangling will set you apart.

Cross-Functional Communication – As a bridge between technical and non-technical stakeholders, your ability to communicate is paramount. You will be evaluated on how well you can explain highly technical economic concepts to product managers, legal teams, and policy leads. Strong candidates articulate the "so what" behind their research, linking data directly to business strategy.

Navigating Ambiguity and Ownership – Uber operates in a rapidly evolving global regulatory environment. Interviewers look for candidates who can take vague, high-level policy questions and translate them into concrete research plans. You must demonstrate a proactive mindset, showing how you independently drive projects from ideation to execution.

4. Interview Process Overview

The interview process for a Research Scientist at Uber is rigorous and multi-faceted, designed to test your theoretical knowledge, coding abilities, and communication skills. You will typically begin with a recruiter phone screen to discuss your background, research experience, and alignment with the role's requirements. This is followed by a technical phone screen, which usually involves a mix of coding (often SQL or data manipulation in Python/R) and foundational statistical or econometric questions.

If you advance, you will be invited to a virtual onsite loop. This intensive stage generally consists of four to five rounds. A hallmark of the Uber research interview is the research presentation, where you will present a past project to a panel of scientists and cross-functional partners. Subsequent rounds will dive deep into causal inference, marketplace economics, behavioral questions, and cross-functional collaboration.

Uber places a strong emphasis on practical application. Interviewers will frequently pivot from theoretical questions to real-world Uber scenarios, asking how you would measure the impact of a new driver incentive or analyze a sudden shift in rider demand.

The timeline above outlines the typical progression from the initial recruiter screen through the final onsite interviews. You should use this visual to pace your preparation, ensuring you balance coding practice with refreshing your theoretical econometrics and refining your research presentation. Note that specific rounds may vary slightly depending on the exact team and your seniority level.

5. Deep Dive into Evaluation Areas

Causal Inference and Econometrics

Because Uber cannot always run randomized controlled trials (A/B tests) due to network effects or regulatory constraints, causal inference is a critical evaluation area. Interviewers want to see that you can extract actionable insights from observational data. Strong performance means knowing exactly which method to apply, understanding its limitations, and explaining how to validate its assumptions.

Be ready to go over:

• Difference-in-Differences (DiD) – How to set up a DiD model, test for parallel trends, and handle staggered rollouts across different cities.
• Instrumental Variables (IV) and Regression Discontinuity (RD) – Identifying valid instruments or cutoffs in a marketplace setting to measure the true effect of a policy change.
• Experimentation with Network Effects – Designing experiments in a two-sided marketplace where treating one group (e.g., drivers) impacts the control group.
• Advanced concepts (less common) – Synthetic control methods, propensity score matching, and structural equation modeling.

Example questions or scenarios:

• "How would you measure the impact of a new minimum wage law for couriers in a specific city using observational data?"
• "If we introduce a new matching algorithm in one neighborhood, how do you account for the spillover effects into adjacent neighborhoods?"
• "Explain a time you used an instrumental variable. What was the instrument, and how did you prove its validity?"

Data Manipulation and Coding

As a Research Scientist, you are expected to be hands-on with data. Uber generates massive amounts of marketplace data, and you must be able to query, clean, and analyze it independently. Interviewers will look for efficiency, accuracy, and familiarity with standard data science libraries.

Be ready to go over:

• Advanced SQL – Complex joins, window functions, CTEs, and optimizing queries for large datasets.
• Data Wrangling in Python/R – Using Pandas, Tidyverse, or similar libraries to clean messy data, handle missing values, and prepare datasets for modeling.
• Applied Statistics in Code – Implementing basic regressions, calculating standard errors, and generating visualizations to communicate results.

Example questions or scenarios:

• "Write a SQL query to find the week-over-week retention rate of new drivers who completed their first trip in January."
• "Given a dataset of rider requests and driver locations, how would you structure a Python script to calculate the average ETA by city and time of day?"

Marketplace Economics and Policy Strategy

Understanding the economics of Uber's platform is essential for the Policy Applied Scientist role. You will be evaluated on your intuition for supply and demand, pricing dynamics, and labor economics. Strong candidates can quickly map a real-world policy issue to economic principles and propose a robust analytical framework.

Be ready to go over:

• Supply and Demand Elasticity – How changes in price or wait times affect rider demand and driver supply.
• Incentive Design – Evaluating the ROI of driver bonuses or courier promotions and understanding their long-term behavioral impacts.
• Platform Regulation – Analyzing the economic trade-offs of policies like earnings guarantees, independent contractor status, or congestion pricing.

Example questions or scenarios:

• "If a city implements a cap on the number of ride-hailing vehicles, what is the expected impact on rider prices, wait times, and driver earnings?"
• "How would you design a research plan to demonstrate the value of flexible work to policymakers?"

Cross-Functional Collaboration and Behavioral

Your research is only valuable if it influences decisions. Uber evaluates your ability to work with product managers, legal counsel, and local policy teams. They are looking for candidates who can navigate conflicting priorities, manage external research partners, and deliver principled advice.

Be ready to go over:

• Stakeholder Management – Aligning different teams on a shared research objective and timeline.
• Communicating Complexity – Distilling complex econometric findings into clear, actionable talking points for non-technical audiences.
• Navigating Ambiguity – Taking a broad, poorly defined question from leadership and turning it into a structured research project.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex statistical concept to a non-technical stakeholder."
• "Describe a situation where your research findings contradicted the initial expectations of the business or policy team. How did you handle it?"