Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Amazon data science interview for a real-estate pricing role. The whole thing was one big open-ended ML system design question that covered basically every stage of the modeling pipeline. More breadth than depth, but you still had to know your stuff at each step.

Questions Asked (1)

Walk through how you'd design a home price estimation model end-to-end, covering how you'd define success metrics, collect and split data, engineer features, choose and validate a model, deploy it, monitor it in production, and handle user complaints when predictions are wrong.

System DesignProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This is a lot to cover in one question and I think I fumbled the ordering a bit.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the ML lifecycle, starting with business understanding and success metrics, then data, modeling, deployment, monitoring, and feedback loops. Emphasize trade-offs and how you'd iterate based on user feedback and business impact.

Pro tip: Tie every technical decision back to business impact and customer obsession—Amazon's leadership principles. Show you can balance model accuracy with latency, cost, and interpretability.

1. Define Success Metrics and Business Objectives

Clarify the goal: accurate home price estimates for users. Define offline metrics (RMSE, MAE) and online metrics (user engagement, conversion, complaint rate). Align with business KPIs like revenue or customer trust.

2. Data Collection, Splitting, and Feature Engineering

Gather data from multiple sources (listings, public records, user interactions). Split temporally to avoid leakage. Engineer features: location, size, amenities, market trends, and interactions. Handle missing values and outliers.

3. Model Selection, Training, and Validation

Choose models based on trade-offs: linear for interpretability, gradient boosting for accuracy. Use cross-validation and holdout sets. Evaluate with metrics and error analysis. Consider ensemble methods.

4. Deployment and Monitoring

Deploy as a scalable API with low latency. Monitor data drift, model performance, and system health. Set up alerts for degradation. Use A/B testing for model updates.

5. Handle User Complaints and Feedback Loop

Investigate complaints: data issues, model bias, or market shifts. Provide explanations and corrections. Use feedback to retrain and improve. Communicate transparently with users.

Key Points to Mention

Temporal data splitting to prevent leakage and simulate real-world forecasting.
Feature engineering with domain knowledge: location, property attributes, market trends, and interactions.
Model trade-offs: interpretability vs. accuracy, latency, and cost.
Monitoring for data drift, concept drift, and performance degradation.
Feedback loops: using user complaints and corrections to retrain and improve the model.
Business impact: aligning metrics with customer satisfaction and revenue.

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