Interview
Questions
Data Science: Decisions and Model Reliability
Practice connecting models to business decisions, evaluating causal claims, investigating production failures, and explaining uncertainty.
Practice these questions directly. An editable copy is yours to change; the original stays unchanged.
Describe a machine learning project you worked on that had significant business impact. What was your approach and what results did you achieve?
Walk me through how you approach model selection for a new problem. What factors do you consider?
A stakeholder says a product change caused an improvement, but no randomized experiment was run. How would you assess that claim, and what would you recommend if the evidence remained uncertain?
Tell me about a time when a model you deployed did not perform as expected in production. How did you diagnose and address the issue?
Tell me about a time you had to consider ethical implications of a model or analysis. How did you approach it?
Tell me about a time when stakeholders disagreed with your analysis or recommendations. How did you handle it?
Describe a project where you had to collaborate closely with engineers, product managers, or other teams. What challenges did you face?