Tell me about a machine learning project you worked on end to end. What was the business problem?

Note: Lead with the business problem, not the model. Interviewers hear "I built an XGBoost classifier" constantly and "we were losing 8% of customers a quarter and could not tell which ones" rarely.

Structure it as:

• The problem and why ML was the right tool. Some problems are better solved with a rule or a report — being able to say why this one was not is a strong signal.
• The data. Where it came from, how much, and what was wrong with it. Realistically most of your time went here, so say so.
• How you framed it. Classification, regression, ranking, or something else, and — critically — what metric you optimised and why that metric matched the business cost.
• What you tried and what you shipped. Include the baseline. A simple model you beat is what makes the result meaningful.
• Deployment and outcome. Whether it reached production, how it was served, and what it actually changed.

If it never reached production, say why. That is a very common and instructive story.