This is the question that exposes you if you've been coasting on vague resume bullets.
Choose a project where you can clearly articulate the problem, your specific contributions, and the measurable business impact. Structure your answer to highlight technical decisions, cross-functional collaboration, and trade-offs, then reflect on what you'd improve and how you validated success with metrics.
Pro tip: Quantify impact in terms of business metrics (e.g., conversion, delivery time, cost) and explicitly connect your ML work to those outcomes—DoorDash values engineers who think beyond model accuracy. Also, be honest about what you'd do differently; it shows growth and self-awareness.
Briefly describe the project, your role, the team, and the business problem it aimed to solve. Keep it concise to leave time for deeper discussion.
Explain the end-to-end process: data collection, feature engineering, model selection, training, evaluation, and deployment. Highlight key technical decisions and trade-offs you made.
Describe how you worked with product, engineering, data science, and other stakeholders to align on goals, gather requirements, and integrate the solution.
Present the metrics that moved (e.g., increased conversion by X%, reduced delivery time by Y minutes) and explain how you measured them. Connect model performance to business outcomes.
Discuss what you would do differently now, such as alternative modeling approaches, better data pipelines, or improved monitoring, and why those changes would matter.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.