The question itself is fine but they really loaded it up.
Use the STAR method to structure your answer, focusing on a specific disagreement with a cross-functional collaborator (e.g., product manager, data scientist, or software engineer). Emphasize how you actively listened to understand their perspective, used data and experimentation to resolve the conflict, and aligned on a solution that balanced technical feasibility with business goals. Conclude with the measurable outcome and a reflection on what you learned.
Pro tip: Frame the disagreement as a difference in priorities or assumptions, not a personal conflict, and highlight how you used data or a small experiment to test both sides' hypotheses. This shows you can turn conflict into a constructive, evidence-based decision-making process—a key skill at DoorDash where ML solutions must balance model performance with business impact.
Briefly describe the project, your role, and the collaborator's role. Clearly state the disagreement in one sentence, focusing on the technical or strategic difference (e.g., model complexity vs. latency, feature prioritization).
Describe how you initiated a private, respectful discussion. Mention that you sought to understand their perspective first by asking open-ended questions and acknowledging their concerns.
Summarize their viewpoint and the underlying interests (e.g., they were worried about user experience or engineering resources). Explain how you validated their concerns and found common ground.
Detail the steps taken to resolve the disagreement, such as running a quick A/B test, building a prototype, or consulting a third party. Emphasize data-driven decision-making and compromise.
State the measurable result (e.g., improved model accuracy, reduced latency, successful launch). Reflect on what you would do differently, such as involving stakeholders earlier or setting up clearer success metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.