2–5 years · Mid — Python · Statistics · scikit-learn · Feature engineering · Model evaluation · Deployment
Review model performance metrics over time to identify trends; check for concept drift using statistical tests; compare recent input data distributions to training data; evaluate external factors influencing predictions; determine if retraining is necessary and plan for data collection.
You discover significant changes in user behaviour affecting the model's primary features. How do you adjust your strategy to mitigate this?
Check data pipeline logs for discrepancies; validate incoming data against expected schema and distribution; assess model performance metrics on historical versus new data; identify any changes in feature engineering due to the data update; decide on immediate retraining or fallback plan.
The updated data includes new categories not present in the training set. How do you handle this categorisation issue?
Considers regularisation techniques like Lasso or Ridge regression; evaluates the complexity of the model and simplifies if necessary; conducts k-fold cross-validation to ensure robustness; looks into feature selection to remove multicollinearity.
What impact does feature scaling have on regularised models?
Utilises data partitioning or sharding to improve access speeds; leverages in-memory data structures or databases; implements efficient data loading techniques such as lazy loading; evaluates batch processing to reduce overhead.
How do you handle feature engineering in a streaming data scenario?
Details a specific feature with its purpose and requirements; explains design choices made and the rationale behind them; discusses key challenges encountered and how they were addressed; reflects on the impact of the feature post-deployment; avoids general statements about teamwork.
What aspects would you improve in future projects?
Specifies the project context and model type; identifies the chosen metric and justifies its relevance to the business problem; discusses alternative metrics considered and why they were not selected; reflects on any surprises during evaluation post-deployment; avoids vague references to team consensus.
How did you validate the effectiveness of your chosen metric?
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