Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Amazon data scientist interview with a meaty end-to-end ML system design question. One question, but it covered basically everything: data pipelines, modeling choices, deployment, the whole stack. Not a casual screen.

Questions Asked (1)

Walk through every step you'd take to build a regression model that predicts daily energy consumption for utility clients, starting from raw data all the way through to production and ongoing monitoring.

System DesignTechnical Trade-offsData Modeling

Author's notes

This one sprawls in a way that's easy to lose the thread on.

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AI HintsAI Generated

Suggested Approach

Structure your answer as an end-to-end ML system design, covering data ingestion, feature engineering, model selection, deployment, and monitoring. Emphasize trade-offs at each stage and how you would handle scale, seasonality, and real-world constraints like missing data and concept drift.

Pro tip: Anchor your answer in business impact: tie model choices to cost, accuracy, and operational constraints, and proactively mention how you'd handle cold-start clients and feedback loops.

1. Data Collection & Understanding

Identify data sources (smart meters, weather, client metadata), assess data quality, and define the prediction target and granularity (daily per client).

2. Feature Engineering & Preprocessing

Create features like lagged consumption, rolling averages, weather variables, calendar effects, and client attributes; handle missing values, outliers, and scaling.

3. Model Selection & Training

Choose appropriate models (e.g., gradient boosting, time-series models) with proper validation (time-based splits), and tune hyperparameters while considering interpretability and scalability.

4. Deployment & Productionization

Package the model for batch or real-time inference, set up CI/CD, and integrate with existing utility systems while ensuring reproducibility and versioning.

5. Monitoring & Maintenance

Track performance metrics (MAE, MAPE), detect data drift and concept drift, and establish retraining triggers and feedback loops for continuous improvement.

Key Points to Mention

Handling seasonality and weather effects with features like heating/cooling degree days
Using time-series cross-validation to avoid data leakage
Addressing cold-start problem for new clients via hierarchical or global models
Scalability considerations for millions of clients (distributed training, efficient inference)
Monitoring for data drift and setting up automated retraining pipelines
Trade-offs between model complexity, interpretability, and latency

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.