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DoorDash·Data Scientist·Onsite - Behavioral / Leadership·Senior

SeniorPrefer not to say
May 2026

Summary

Behavioral round at DoorDash for a data scientist role, focused heavily on how you work with non-technical people and how you decide what to work on when everything feels urgent. Pretty standard stuff but the prioritization angle had some teeth to it.

Questions Asked (2)

Q1

Tell me about a time you got a non-technical stakeholder to actually act on your recommendation. What did you do to bring them along?

Stakeholder ManagementCross-functional Alignment
Author's notes

I went with a story about convincing a product manager to delay a launch based on some retention modeling I'd done.

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AI HintsAI Generated

Suggested Approach

Choose a specific example where your data-driven recommendation initially faced resistance from a non-technical stakeholder. Walk through how you diagnosed their concerns, translated your insights into their language, and built trust to drive action. Emphasize the outcome and what you learned about influencing without authority.

Pro tip: Focus on how you made the stakeholder feel heard and involved in the decision, rather than just presenting data. Show that you adapted your communication style to their priorities and concerns.

1. Set the Context

Briefly describe the project, the stakeholder's role, and why your recommendation was important. Highlight the initial gap between your data-driven view and their perspective.

2. Diagnose Resistance

Explain how you uncovered the root of their hesitation—whether it was lack of understanding, competing priorities, or distrust in data. Show empathy for their position.

3. Tailor Your Approach

Describe how you adapted your communication: using analogies, focusing on business impact, or co-creating solutions. Emphasize collaboration over persuasion.

4. Drive Action

Detail the specific steps you took to move from agreement to action, such as running a pilot, providing ongoing support, or aligning with their goals.

5. Reflect on Impact

Share the results—quantify if possible—and what you learned about stakeholder management that you've applied since.

Key Points to Mention

  • Active listening to understand the stakeholder's true concerns and priorities
  • Translating technical findings into business language and tangible outcomes
  • Building trust through transparency, empathy, and incremental wins
  • Leveraging data storytelling and visualization to make insights accessible
  • Aligning your recommendation with the stakeholder's own goals and incentives
  • Following up with support and celebrating shared success to reinforce the relationship

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

Q2

When you have multiple data science projects or requests competing for your time, how do you decide what gets done first?

Roadmap PrioritizationAdaptability & Ambiguity
Author's notes

Blanked a little on the structure here.

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AI HintsAI Generated

Suggested Approach

Start by describing a structured prioritization framework that balances business impact, urgency, and effort. Then, walk through a specific example where you applied this framework to resolve competing priorities, highlighting collaboration with stakeholders and data-driven decision-making. Conclude by emphasizing your ability to adapt when priorities shift.

Pro tip: At DoorDash, where speed and customer experience are critical, prioritize projects that directly impact key metrics like delivery time or order volume. Show that you understand the business context and can make trade-offs that align with company goals.

1. Assess Business Impact

Evaluate each project's potential impact on key business metrics (e.g., revenue, customer retention, operational efficiency). Quantify the expected value where possible.

2. Consider Urgency and Dependencies

Determine deadlines, stakeholder expectations, and dependencies. Some projects may be time-sensitive or block other critical work.

3. Estimate Effort and Resources

Estimate the time, data, and personnel required for each project. Consider your team's capacity and skill sets.

4. Align with Stakeholders

Discuss priorities with stakeholders to ensure alignment and manage expectations. Use data to support your recommendations.

5. Decide and Communicate

Make a decision based on the above factors, and clearly communicate the rationale and trade-offs to all involved parties.

Key Points to Mention

  • Use of a prioritization framework (e.g., RICE, ICE, or impact/effort matrix)
  • Alignment with company OKRs and business goals
  • Stakeholder communication and expectation management
  • Data-driven decision making (e.g., using metrics to quantify impact)
  • Ability to adapt when priorities change (e.g., re-prioritize based on new information)
  • Example of a past situation where you successfully prioritized competing projects

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