Interview Guide

Uber Data Scientist Interview Guide (2026)

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The role of an Uber Data Scientist

A Uber Data Scientist is responsible for analyzing large amounts of data to help the company make informed business decisions. They use a variety of tools and techniques to collect, process, and analyze data from various sources, such as GPS data from Uber's app, customer ratings and feedback, and economic data.

One of the main responsibilities of a Uber Data Scientist is to use this data to identify patterns and trends that can be used to improve the overall performance of the company. For example, they may analyze data on driver and rider behavior to identify areas where the company can improve its matching algorithm or optimize pricing strategies. They also use data to identify new opportunities for growth and expansion, such as identifying cities where Uber is not yet available but where there is a high demand for ride-hailing services.

In addition to analyzing data, a Uber Data Scientist also plays a key role in developing and implementing new data-driven products and features. For example, they may work with engineers and product managers to design and build new data-driven features such as Uber's "destination filter" which allows riders to set a destination before requesting a ride.

Uber hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior and Staff level roles and some openings for Data Science Managers along with some Applied Scientist roles. Consider reviewing the Walmart Data Engineer Interview Guide and Waymo Data Scientist guides for a broader perspective.

Overall, the role of a Uber Data Scientist is to use data and analytics to drive business growth and improve the overall user experience for Uber's riders and drivers.

How to Apply for a Data Scientist Job at Uber?

To apply for a Data Scientist job at Uber, browse the job listings on Uber's career website and find the data scientist position that best matches your qualifications and experience. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.

It's important to note that the application process may vary depending on the position and location, so you should always check the specific job listing for more information on the application process.

Responsibilities of a Data Scientist at Uber

The responsibilities of a data scientist at Uber across roles can broadly be seen as-

  • Creating practical data insights and solutions from Uber’s large datasets.
  • Employing a mastery of statistical analysis, including descriptive statistics, correlation, regression, and confidence intervals.
  • Designing metrics and developing SQL queries to generate reports.
  • Generating ideas for exploratory analysis for a range of use cases. For instance to shape paid marketing strategies.
  • Use machine learning, experimentation, causal learning, time series analysis, natural language processing, and more to develop and lead statistical and machine learning efforts for different support systems of Uber.
  • Conducting feasibility studies to analyze the impact of proposed product changes.
  • Communicating your findings to cross-functional peers and management.

Skills and Qualifications needed for Data Scientists at Uber

Here are some of the skills and qualifications that may be required for a Data Scientist at Uber. One thing to note here is that the degree qualification (bachelor’s/ masters’) is different for every role.

  • It's mandatory to have at least 5+ years of experience in some Data Science roles.
  • Make sure you have advanced SQL expertise. This will be crucial for working with the large and complex data sets that are common in this role.
  • A basic understanding of causal inference, experimental design (such as A/B experiments) and statistical methods will be important for being able to analyze data effectively and guide teams on making data-informed decisions.
  • Ability to extract insights from data and summarize learnings and takeaways. This will be important for communicating your findings to stakeholders and leadership.
  • Experience with Excel and some form of dashboarding or data visualization tool (such as Tableau, Mixpanel, Looker, or similar) is a plus as it will help you to effectively present your findings.
  • Having experience launching productionized machine learning models at scale will give you an edge over the other candidates.

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Uber Data Scientist Interview Guide

The Uber Data Scientist Interview typically emphasizes experimentation and product analytics over machine learning engineering. Many candidates misunderstand this distinction, preparing for algorithm questions when Uber interviews often prioritize SQL proficiency, A/B test design, and marketplace intuition. The data science organization at Uber handles metric investigation and causal inference while Applied Scientists and ML Engineers own the modeling work. Understanding this role separation before interviewing is critical.

Prepfully coaches who have worked at Uber consistently tell us the same thing: Data Scientists at Uber function primarily as product analysts, not ML researchers. Applied Scientists and ML Engineers handle deep learning and model development. Data Scientists own experimentation, causal inference, and metric investigation. Candidates who lean heavily on algorithm trivia often struggle, while those with demonstrated SQL and experimentation expertise tend to advance.

What follows draws from Prepfully coaches with Uber hiring experience and candidates who completed the process recently, covering each interview stage, technical depth expectations, and targeted preparation approaches.

Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview.

How the Interview Process Works

The interview process typically takes 3-6 weeks from first recruiter contact to offer, with most candidates completing in around 4 weeks. Uber generally follows a 5-stage model for most teams, though Prepfully candidates told us timelines and structure can vary based on seniority level, team needs, and hiring urgency.

Uber data scientist guide by Prepfully

Recruiter Phone Screen (30-60 minutes)

Overview

This conversation explores your technical background, motivation for Uber, and past experience with data-driven decision-making. Recruiters look for evidence of statistical analysis, experimentation, and business impact in your prior work. Prepfully candidates reported that recruiters often discuss compensation expectations and provide transparency on leveling during this call, though this varies by recruiter.

Prepfully candidates told us that framing past work through measurable outcomes tied to Uber-relevant metrics (rider growth, driver retention, marketplace efficiency) resonates more than describing technical implementations without business context.

If you are also interviewing for similar roles at other companies, the Meta Data Scientist interview guide covers comparable experimentation and product analytics expectations.

Technical Screen (45-60 minutes)

Overview

This is a live coding round where candidates work with a senior data scientist on CodeSignal or CoderPad. The assessment combines SQL queries and statistical concepts. According to Prepfully candidates, SQL typically carries the most weight in this round.

The SQL portion typically includes medium-level questions on data manipulation, joins, and window functions, though difficulty can vary by interviewer. Candidates should be prepared for harder questions as well. Common topics include:

  • Finding a user's Nth transaction using ROW_NUMBER
  • Calculating time delays between events using LAG/LEAD
  • Aggregating conversion metrics with temporal constraints
  • Multi-table joins on rides, users, and transaction data

The statistical portion covers A/B testing fundamentals, p-value interpretation, and experiment design principles. One example Prepfully candidates encountered: explaining how you would design an experiment to test whether surge pricing improves driver utilization. Note that not all teams are marketplace-focused, so examples may vary.

Prepfully candidates told us that thinking aloud throughout problem-solving, explaining your approach, surfacing assumptions, and testing boundary conditions tends to perform better than silent coding followed by a final answer reveal.

Uber data scientist guide by Prepfully

Statistics and Experimentation Round (45-50 minutes)

Overview

This round focuses on experimental design and causal inference. Uber runs thousands of experiments simultaneously, and data scientists are expected to design rigorous experiments and critically analyze findings. This is one of Uber's formalized interview formats that Prepfully candidates report encountering consistently.

This round frequently includes the following topics, according to Prepfully candidates:

  • Designing A/B tests for marketplace features with appropriate metrics and guardrails
  • Understanding when A/B tests fail in two-sided marketplaces due to spillover effects
  • Explaining CUPED (Controlled-experiment Using Pre-Experiment Data) for variance reduction
  • Discussing switchback experiments for algorithmic changes affecting shared resources

Prepfully candidates frequently encounter this scenario: "Your manager tested 20 different pricing strategies simultaneously and found significance in one variant. How would you interpret this finding?" This evaluates understanding of multiple comparison corrections and Type I error inflation.

The methods used on a large-scale basis by Uber are discussed on their official blog on causal inference. Those applicants that mention this material demonstrate that they are aware of the experimentation standards at Uber.

I would do an experiment, a user level experiment. Unless I can't do it. If I can't do it, I would then look for like a geo-based experiment. Unless I can't do it. If I can't do a geo-based experiment, then I'm thinking, okay, maybe there's a switchback.

Product Case Round (45-50 minutes)

Overview

This round simulates day-to-day data science tasks at Uber. Interviewers present ambiguous business problems and evaluate how you break them down, define metrics, design experiments, and navigate trade-offs.

Marketplace-specific cases Prepfully candidates have seen:

ETA Prediction System Design: The DeepETA engineering blog of Uber states that the system receives 500,000 requests per second with less than 5ms latency requirements. The candidates are expected to talk about feature engineering (map segments, temporal patterns, real-time traffic), model architecture decisions and drift monitoring.

Driver Churn Analysis: Driver retention is a known challenge for rideshare platforms. Interviewers want to see how you would identify leading indicators from early driving data, including earnings per hour, work consistency, and engagement trends.

Surge Pricing Optimization: Designing pricing algorithms that balance demand-supply ratios, revenue optimization, and rider experience. This tests your understanding of real-time systems and economic trade-offs.

Rider Wait Time Reduction: Breaking down wait time into components (driver availability, matching, routing, traffic) and proposing experiments to test targeted interventions.

Effective responses demonstrate structured problem-solving: begin by clarifying business context and constraints, establish primary metrics with appropriate guardrails, outline analytical methodology, then acknowledge limitations. Uber prioritizes actionable recommendations that teams can implement over academically perfect but impractical analysis.

For similar marketplace analytics preparation, the DoorDash Data Scientist interview guide covers comparable case study formats.

Behavioral and Hiring Manager Round (30-45 minutes)

Overview

The final round assesses values alignment and working style fit. A hiring manager or senior team member evaluates how you handle ambiguity, collaborate with teams, and communicate findings to stakeholders. Prepfully candidates reported that interviewers look for ownership mentality, bias toward action, and comfort making decisions with incomplete information.

Common questions Prepfully candidates reported:

  • "Describe a time you used data to influence a business decision"
  • "Tell me about a disagreement with a teammate and how you resolved it"
  • "Share an example where you had to learn a new technology quickly"
  • "How do you handle critical feedback?"

Uber looks for analytical rigor paired with bias toward action. Candidates who demonstrate comfort with ambiguity while using data to reduce it perform well. The interview values balanced approaches that combine data-driven insights with pragmatic recommendations.

What Technical Rounds Cover

SQL Expectations

SQL carries significant weight in Uber DS interviews. You can get away with rough edges in a few places, but SQL is not one of them. It appears across rounds, and when it's shaky, the interview usually does not recover. Prepfully candidates told us weak SQL performance is one of the most common reasons for rejection.

Interviewers expect mastery of:

  • Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD with PARTITION BY
  • CTEs (Common Table Expressions) for complex multi-step queries
  • Self-joins and correlated subqueries
  • Performance considerations for billion-row tables
  • Edge case handling for nulls, timezones, and refunds

Technical screens typically include multiple SQL problems heavily focused on window functions (PARTITION OVER/LEAD/LAG), according to Prepfully candidates. Weak optimization skills in this area can hurt your overall evaluation.

Python and Data Manipulation

Python questions assess data manipulation proficiency and algorithmic thinking. Uber expects:

  • Pandas operations: groupby, merge, pivot, apply
  • List comprehensions and lambda functions
  • Basic algorithmic problems at LeetCode medium level
  • Feature engineering for time-series and transactional data

Pair programming sessions weight collaboration equally with technical correctness. Articulate your reasoning throughout, validate solutions with test cases, and discuss time-space complexity implications for Uber's data volume.

Statistics and Experimentation

Experimentation questions go deeper than standard A/B testing. Uber's sophisticated experimentation platform requires data scientists to understand:

CUPED Method: Leveraging pre-experiment covariate data to reduce outcome variance, enabling smaller sample sizes and faster decisions. You should understand the intuition behind why adjusting for baseline behavior improves experiment sensitivity, though deep mathematical derivation is not typically expected.

Switchback Experiments: For algorithmic changes that affect shared resources (matching, surge pricing), Uber uses time-split experiments rather than user-split A/B tests. Treatment and control alternate over time intervals.

Spillover Effects: In two-sided marketplaces, treating one side affects the other. A rider incentive changes driver supply, which impacts control group riders. Understanding this interference is critical.

Multiple Comparison Corrections: With thousands of concurrent experiments, controlling false discovery rate matters. Bonferroni and Benjamini-Hochberg methods come up frequently.

To strengthen experimentation foundations, Prepfully's Data Science Interview Course covers A/B testing frameworks and causal inference methods, co-created by senior data science leaders from FAANG companies.

Uber data scientist guide By Prepfully

Compensation Ranges

According to levels.fyi data for US-based roles (compensation changes frequently, treat as approximate), total compensation at Uber varies by level:

  • DS I (L3): $165K-$170K total ($135K-$140K base, $18K-$22K stock/year, $8K-$10K bonus)
  • DS II (L4): $260K-$270K total ($160K-$165K base, $75K-$85K stock/year, $20K-$25K bonus)
  • Senior DS I (L5a): $335K-$345K total ($185K-$195K base, $120K-$130K stock/year, $25K-$28K bonus)
  • Senior DS II (L5b): $460K-$475K total ($205K-$215K base, $220K-$235K stock/year, $26K-$30K bonus)

    RSU vesting follows a front-loaded schedule. The standard structure vests 35% in year one, 30% in year two, 20% in year three, and 15% in year four. Some offers include an accelerated plan with 55% vesting in year one.

Negotiation dynamics: Prepfully candidates reported that base salary negotiation at Uber tends to be limited due to narrow salary bands. Sign-on bonuses (typically 2-6 months of base salary) and equity are generally more negotiable. Competing written offers provide significant leverage.

What Makes Candidates Successful

Based on patterns Prepfully candidates shared about their interview experiences:

SQL proficiency is foundational: Communicating your approach while coding and validating edge cases tends to resonate with interviewers. Window function optimization in particular carries significant weight according to Prepfully candidates.

Experimental rigor beyond basics: Understanding causality versus correlation, identifying potential confounders, and recognizing network effects in marketplace contexts resonates with interviewers. Framing A/B tests within two-sided marketplace constraints, rather than treating them as simple randomized trials, tends to perform well.

Business-aligned thinking: Connecting metrics to business outcomes demonstrates ownership mentality. This means considering both volume metrics (total trips, active users) and unit economics (driver profitability, take rate, customer lifetime value) rather than focusing on one dimension alone. Thinking holistically about how metrics interact tends to resonate with Uber interviewers.

Marketplace fluency: Understanding how Uber's two-sided platform works, including surge pricing mechanics, driver supply dynamics, and demand-supply balancing, helps you contextualize case studies effectively. Even for teams that are not directly marketplace-focused, Uber places significant emphasis on this mental model. Prepfully coaches told us this may reflect how central marketplace thinking has been to Uber's success.

Leveling considerations: Uber can assess candidates conservatively compared to some tech companies. Prepfully candidates have occasionally been leveled below their expectations, though this depends on many factors beyond years of experience, including system design depth, communication skills, and interview performance. Years of experience alone does not determine level.

How to Prepare

SQL (Priority: Critical)

Complete 50+ targeted problems emphasizing the SQL concepts covered in the Technical Screen section above, with particular focus on:

  • Complex partitioning scenarios beyond basic window functions  
  • Temporal aggregations: rolling windows, running calculations
  • Edge case validation: null handling, timezone conversions, duplicate detection

DataLemur and StrataScratch and Prepfully maintain Uber-specific SQL problems with realistic schemas reflecting their data model.

Experimentation (Priority: Critical)

Master Uber's experimentation methodology:

  • Study Uber's causal inference engineering blog for production implementation details
  • Learn switchback experiment design for marketplace algorithms where spillover invalidates standard A/B tests
  • Work through CUPED variance reduction calculations and statistical power computations
  • Practice Bonferroni and Benjamini-Hochberg corrections for concurrent experiments

Product Sense (Priority: Medium)

Develop marketplace intuition specific to Uber's business model:

  • Master core metrics: GMV calculation, take rate economics, driver utilization rates, rider cohort retention
  • Understand surge pricing algorithms: how demand-supply imbalances trigger dynamic pricing and affect both sides
  • Practice metric investigation frameworks: decomposing changes, isolating root causes, proposing targeted experiments

Practice under time limitations and expert feedback is significantly better at interview preparation. Set up simulated case studies, SQL problems, and experimentation conversations by scheduling mock interviews with Prepfully coaches.

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The Uber Data Scientist Interview tl;dr

SQL carries disproportionate weight. Prepfully candidates consistently report that SQL proficiency, particularly window functions, is one of the most critical technical skills evaluated. Small hiccups in SQL can have outsized impact because it is considered fundamental for Data Scientists to have mastery over.

This role typically focuses on product analytics and experimentation rather than ML engineering. Applied Scientists and MLEs handle deep learning and model development. Data Scientists design A/B tests, investigate metric movements, and translate findings for stakeholders. Causal inference knowledge tends to be more valuable than algorithm theory for most DS roles, though this can vary by team.

Total compensation ranges from approximately $165K (L3) to $475K (L5b) for US-based roles. RSU vesting is front-loaded (35% year one, 30% year two, 20% year three, 15% year four). Base salary is typically hard to negotiate due to narrow bands. Prepfully candidates were more successful focusing negotiation on sign-on bonuses and equity.

Work intensity varies by team. Prepfully candidates reported that revenue-critical teams (Rides optimization, surge pricing, fraud detection) tend to have higher workloads than newer or support-focused divisions. Actual hours vary significantly by team, manager, and project phase.

The process takes 3-6 weeks across 5 rounds: recruiter call, technical screen, experimentation deep dive, product case study, and behavioral assessment. Preparation should prioritize SQL window functions, CUPED methodology, and marketplace-specific case studies over LeetCode grinding.

For one-on-one guidance from data scientists with Uber experience, explore Prepfully's coaching network.

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