Think about a situation where a Python-based model or analysis you developed did not perform as expected in production or in a live test. How did you discover the issue, communicate it to your team, and what steps did you personally take to fix it?

Describe a situation where you had to balance writing clean, maintainable Python code with delivering a data science solution under tight deadlines. How did you prioritize, and what trade-offs did you make or push back on?

Describe a time when your data-driven recommendation, built in Python (e.g., from a model, analysis, or dashboard), was initially challenged or resisted by stakeholders. How did you handle the pushback and what was the final outcome?

Tell me about a recent data science project where you used Python end-to-end (from data cleaning to model deployment). What was your specific role on the team, and how did you collaborate with others (engineers, product, stakeholders) to get it delivered?

Give an example of a time you had to explain a complex Python/ML workflow (e.g., feature engineering, model selection, or evaluation metrics) to a non-technical audience. How did you adapt your communication style, and how did you know they truly understood your message?