Build model to predict package delivery time

Q: Build model to predict package delivery time

This question evaluates a candidate's competence in end-to-end machine learning system design for time-to-event forecasting, covering prediction target definition and timing, feature engineering and model selection, handling missing or delayed signals and data leakage, uncertainty quantification, and production monitoring and retraining.

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Question

You are building an ML model to predict package delivery time (ETA) for shipments.

Given historical shipping data (order created time, origin/destination, carrier/service level, scan events, weather, traffic, holiday seasonality, etc.), propose an approach to:

Define the prediction target and when the prediction is made (e.g., at label creation vs after pickup vs in-transit).
Choose model type(s) and features.
Handle missing/late scan events and data leakage.
Evaluate the model offline and online.
Provide calibrated uncertainty (prediction intervals) and how you would use it in product.
Monitor and retrain the model in production.

Build model to predict package delivery time

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