Choose one data-science project that is relevant to the role and walk a hiring manager through it. Explain the problem, your role, the analytical approach, how you validated the work, and the decision or outcome. Make clear what you personally owned. ### Constraints & Assumptions - Use a project you can discuss without sharing confidential information. - Keep the initial answer to three or four minutes, with technical depth available for follow-ups. - Do not present a model metric as the final business outcome unless the connection is justified. ### Clarifying Questions to Ask - Would you prefer a project that emphasizes product analytics, experimentation, or modeling? - How much technical depth would be most useful in the initial walkthrough? ### What a Strong Answer Covers - The decision, stakeholder, unit of analysis, and success criterion. - Your individual contribution to framing, data quality, method, validation, and communication. - Why the chosen approach fit the problem and what alternative you considered. - Results, uncertainty, limitations, and what happened after the recommendation. ### Follow-up Questions 1. What was the largest source of bias or uncertainty? 2. Which result changed a stakeholder's view? 3. What would you redesign if you had twice the time?