Design a robust traffic forecasting pipeline
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
Overview: This question evaluates a candidate's competency in end-to-end time-series forecasting pipeline design, covering data cleaning and missing-value handling, anomaly detection and intervention strategies, feature engineering, probabilistic modeling with Unobserved Components Models, rolling-origin backtesting, model comparison, and scaling for multiple related series. It is commonly asked to assess practical and conceptual understanding of Machine Learning and time-series forecasting — including model assumptions, uncertainty quantification, evaluation metrics for quantiles, and productionization considerations — and tests both conceptual understanding and practical application within the Machine Learning / Time-Series Forecasting domain.
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