Uber Data Scientist Interview Guide

After applying, candidates first connect with someone from Uber’s talent team, then meet the person leading the work.

• Technical evaluation: For technical roles, Uber includes a step focused on how candidates build and solve problems.
• Job-related exercise: Some roles include an analytics task, written exercise, portfolio review, or work simulation.
• Team conversations: Candidates meet teammates and cross-functional partners.
• Performance review: Recruiters partner with the hiring team to review performance against criteria for the job.

For preparation, treat screen, take-home, coding, case, behavioral, and onsite as practice categories, not Uber’s official round names.

• Screen: Explain your background, analytical choices, and interest in the work clearly and concisely.
• Take-home: Structure an analysis, state assumptions, check data quality, and present a defensible conclusion.
• Coding: Practice SQL, data manipulation, logical correctness, and edge-case checks.
• Case: Define metrics, design experiments, interpret uncertainty, and make sound modeling decisions.
• Behavioral: Give evidence of collaboration, judgment, ownership, and communication with non-experts.
• Onsite: Practice moving between technical detail, product reasoning, and clear recommendations.

Data Scientist preparation sits between technical execution and decision-making. Compared with general software engineering practice, it places more emphasis on statistics, experimental design, metrics, and interpretation. Compared with reporting-focused analytics practice, it requires a stronger defense of model choice, uncertainty, and tradeoffs.

Because Uber candidates may meet cross-functional partners, present each example at two levels: explain the technical reasoning, then translate it into a decision a non-expert can evaluate.

Uber does not publish how long the process takes. Ask the talent-team contact about the sequence, scheduling, and whether the role includes a job-related exercise.

For a Data Scientist candidate, the challenge is breadth: statistics, experimentation, SQL, coding, modeling judgment, and communication can require different modes of thinking. Practice switching between them while keeping assumptions and conclusions consistent.

• Map your evidence: Choose work examples that show problem definition, analytical judgment, collaboration, and measurable consequences.
• Review experimentation: Practice hypotheses, metric selection, randomization, bias, uncertainty, and interpretation.
• Strengthen technical fluency: Write readable SQL, explain data transformations, and test edge cases.
• Defend modeling choices: Compare methods using assumptions, interpretability, validation, and the cost of errors.
• Practice case communication: Begin with the decision, identify missing information, and separate findings from recommendations.
• Rehearse an exercise: Complete an analysis or work simulation, then review whether another person could follow your reasoning without extra context.
• Prepare behavioral stories: Show what you did, why you chose that path, how you worked with others, and what you learned.