I had a decent story for this but I fumbled the resolution part.
Use the STAR method to tell a concise story about a specific disagreement with a cross-functional partner (e.g., product, engineering, or marketing). Focus on how you used data and empathy to understand their perspective, proposed a collaborative solution, and achieved a positive outcome for the business.
Pro tip: Emphasize that you sought to understand their incentives and constraints first, and that you framed the disagreement as a shared problem rather than a personal conflict. This shows maturity and stakeholder management skills.
Briefly describe the project, your role, and the cross-functional partner involved. Highlight why the disagreement mattered to the business.
Clearly state the opposing viewpoints. Focus on the issue, not the person, and show that you understood their perspective.
Explain how you listened, gathered data, and proposed a solution. Emphasize collaboration and data-driven decision making.
Detail how you reached alignment, including any compromises or experiments. Mention the partner's role in the solution.
Quantify the impact (e.g., improved model accuracy, increased revenue, faster delivery) and reflect on what you learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to tell a concise story about a time you faced resource or data constraints. Emphasize how you prioritized, creatively sourced or approximated data, and delivered a measurable impact. Highlight the trade-offs you made and the lessons learned.
Pro tip: Quantify the impact even if data was limited—use proxies or estimates, and clearly state assumptions. Show that you can make decisions with imperfect information, a key skill at DoorDash.
Briefly describe the project, the business goal, and the specific resource or data constraints you faced (e.g., missing data, limited engineering support, tight timeline).
Explain how you identified the highest-impact question or metric to focus on given the constraints, and how you scoped the work to be achievable.
Detail the concrete steps you took: e.g., using proxy metrics, manual data collection, leveraging existing logs, building a lightweight model, or collaborating cross-functionally.
Describe how you quantified the impact (even with estimates) and communicated it to stakeholders, including any caveats or assumptions.
Share what you learned about working under constraints and how it improved your approach or the team's processes for future projects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a specific instance where feedback led to a measurable change in your data science work, such as improving model interpretability or stakeholder communication. Use the STAR method to structure your response, emphasizing the feedback, your actions, and the positive outcome. Highlight how you applied the feedback to future projects, showing growth and adaptability.
Pro tip: Select feedback that aligns with DoorDash's values, such as being customer-obsessed or operating with a growth mindset, and quantify the impact of your change to demonstrate business acumen.
Briefly describe your role, the project, and the situation to give the interviewer a clear picture of the environment in which the feedback was given.
State the critical feedback you received from your manager, focusing on the specific behavior or output that needed improvement, such as model documentation or stakeholder alignment.
Detail the steps you took to address the feedback, including any new processes, tools, or communication strategies you adopted, and how you sought guidance or resources.
Share the positive results of your changes, using metrics if possible, such as improved model performance, faster iteration, or better stakeholder satisfaction.
Explain how you internalized the feedback and applied it to future projects, demonstrating continuous learning and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.