Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Amazon data scientist interview focused on a deep end-to-end walkthrough of a real ML project. The question was broad but they clearly wanted specifics, not a surface-level story.

Questions Asked (1)

Walk me through a project where you applied machine learning or statistical modeling. What features did you engineer, what was the final output, and looking back, what would you change to get better results or more business impact?

Product Analytics & MetricsTechnical Trade-offsRoot Cause Analysis

Author's notes

This question sounds open-ended but it's really a trap for people who tell a tidy success story.

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AI HintsAI Generated

Suggested Approach

Choose a project where you can clearly articulate the business problem, your technical approach, and the measurable impact. Structure your answer using a STAR-like format, focusing on feature engineering, model output, and a candid retrospective on improvements. Emphasize how your work aligned with business goals and what you learned.

Pro tip: Quantify the business impact (e.g., revenue lift, cost savings) and tie your retrospective to Amazon's leadership principles like Customer Obsession and Invent & Simplify. Show that you think like an owner, not just a modeler.

1. Set the Context

Briefly describe the business problem, the project's goal, and your role. Highlight why it mattered to the business or customers.

2. Explain Feature Engineering

Detail the features you created or selected, including data sources, transformations, and domain knowledge applied. Mention any feature importance analysis.

3. Describe the Model and Output

Summarize the modeling technique, validation strategy, and final output (e.g., predictions, insights). Explain how the output was used or deployed.

4. Quantify Business Impact

Share measurable results: improved metrics, ROI, or customer impact. Connect the model's performance to business outcomes.

5. Reflect on Improvements

Discuss what you would change to enhance results or business impact, such as additional data, better features, or a different modeling approach. Show self-awareness and growth mindset.

Key Points to Mention

Business problem and success metrics (e.g., increase conversion, reduce fraud)
Feature engineering techniques and rationale (e.g., aggregations, embeddings, domain-specific features)
Model selection, validation, and performance metrics (e.g., AUC, RMSE, precision/recall)
Deployment or integration of the model into a product or decision process
Quantified business impact (e.g., 10% increase in sales, $1M cost savings)
Retrospective improvements (e.g., more data, better feature store, A/B testing, monitoring)

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