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Uber·Data Scientist·Hiring Manager Screen·Senior

Senior
Jun 2026

Summary

Behavioral screen for a Data Scientist role at Uber, focused on leadership potential and cultural fit. The hiring manager wanted to see how you think about your own work and whether you can operate without a title backing you up.

Questions Asked (3)

Q1

Walk me through your resume and highlight the project you are most proud of.

Product Analytics & MetricsAdaptability & Ambiguity
Author's notes

I picked a project with decent numbers and talked through it as situation-action-result.

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AI HintsAI Generated

Suggested Approach

Give a concise chronological overview of your resume, emphasizing roles and skills relevant to data science at Uber. Then dive deep into one project you're most proud of, using a structured story that highlights your technical contributions, the impact on product metrics, and how you navigated ambiguity. Connect the project to Uber's focus on product analytics and adaptability.

Pro tip: Choose a project where you can clearly quantify the business impact (e.g., increased conversion by X%, reduced latency by Y%) and explicitly tie it to Uber's key metrics like trips, ETAs, or driver utilization. This shows you think like an Uber data scientist.

1. Brief Resume Walkthrough

Summarize your career progression in 1-2 minutes, focusing on data science roles and projects that align with Uber's needs. Highlight transitions and key skills gained.

2. Select the Project

Choose a project that demonstrates both technical depth and business impact, ideally involving product analytics, experimentation, or ambiguity. State why it's meaningful to you.

3. Set the Context

Describe the problem, the team, and the goals. Explain why it was ambiguous or challenging, and what was at stake.

4. Detail Your Actions

Walk through your approach: data collection, analysis, modeling, experimentation, and collaboration. Highlight technical tools and methods used.

5. Quantify Impact and Learnings

Share measurable outcomes (e.g., metrics improved, revenue generated) and reflect on what you learned, especially about handling ambiguity and driving product decisions.

Key Points to Mention

  • Quantifiable business impact (e.g., increased user engagement, reduced costs, improved model accuracy)
  • Use of product analytics metrics (e.g., A/B testing, funnel analysis, cohort analysis)
  • Adaptability to ambiguity: how you defined the problem, iterated, and made decisions with incomplete data
  • Technical skills: SQL, Python, machine learning, experimentation, causal inference
  • Collaboration with cross-functional teams (product, engineering, operations)
  • Alignment with Uber's values and data-driven culture

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Tell me about a time you received critical feedback. How did you respond?

Adaptability & AmbiguityConflict Resolution
Author's notes

Blanked for a second and picked a story that was a little too safe.

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AI HintsAI Generated

Suggested Approach

Choose a specific instance where you received critical feedback on a data science project, ideally one that highlights your ability to adapt and improve. Use the STAR method to describe the situation, the feedback, your response, and the positive outcome. Emphasize how you turned the feedback into a learning opportunity and demonstrated growth.

Pro tip: Show that you not only accepted the feedback but also proactively sought additional input and implemented changes that had a measurable impact. This demonstrates ownership and a growth mindset, which are highly valued at Uber.

1. Set the Context

Briefly describe the project or situation where you received critical feedback, including your role and the stakes involved.

2. Describe the Feedback

Clearly state what the feedback was, who gave it, and why it was important. Be specific and avoid vague statements.

3. Explain Your Response

Detail how you reacted initially and what actions you took to address the feedback. Show that you listened, asked clarifying questions, and developed a plan.

4. Highlight the Outcome

Describe the results of your actions, including improvements in your work, positive feedback, or measurable impact on the project or team.

5. Reflect and Learn

Summarize what you learned from the experience and how it has influenced your approach to similar situations since then.

Key Points to Mention

  • Specific example of critical feedback related to data science work (e.g., model performance, communication of results, collaboration).
  • Your initial emotional reaction and how you managed it professionally.
  • Actions taken to understand and address the feedback (e.g., seeking mentorship, additional training, revising approach).
  • Quantifiable improvement or outcome resulting from your response.
  • How you applied the learning to future projects or roles.
  • Demonstration of a growth mindset and commitment to continuous improvement.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q3

Describe a situation where you had to influence stakeholders without having formal authority over them.

Stakeholder ManagementCross-functional Alignment
Author's notes

This one is basically asking if you can lead without a title, which is a real thing at a company like Uber where data scientists are expected to drive decisions cross-functionally.

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AI HintsAI Generated

Suggested Approach

Use the STAR method to structure your answer, focusing on a specific situation where you had to influence stakeholders without formal authority. Highlight how you built credibility through data-driven insights, tailored your communication to different stakeholders, and achieved a mutually beneficial outcome.

Pro tip: Emphasize how you leveraged data to tell a compelling story that aligned with stakeholders' goals, and quantify the impact of your influence (e.g., adoption rate, revenue impact).

1. Set the Context

Briefly describe the situation, including the stakeholders involved, the project's importance, and why you lacked formal authority.

2. Identify Stakeholder Needs

Explain how you analyzed each stakeholder's priorities, concerns, and motivations to tailor your approach.

3. Build Credibility with Data

Describe how you used data, prototypes, or pilot results to demonstrate the value of your proposal and build trust.

4. Communicate and Align

Detail your communication strategy, including how you framed benefits, addressed objections, and fostered collaboration.

5. Achieve and Measure Impact

Summarize the outcome, highlighting stakeholder buy-in, implementation, and quantifiable results.

Key Points to Mention

  • Use of data-driven storytelling to persuade stakeholders
  • Adaptation of communication style to different stakeholder groups (e.g., technical vs. non-technical)
  • Building trust through transparency and active listening
  • Leveraging cross-functional relationships and allies
  • Quantifiable impact of the influence (e.g., adoption rate, revenue increase, efficiency gains)
  • Lessons learned and how you would apply them in future situations

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.