Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

SQL-heavy technical screen for a DS role at Amazon, focused on logistics/supply chain analytics. One question, but it had enough layers to keep me busy for a while.

Questions Asked (1)

For a shipment defect monitoring system, what metrics would you define to track quality on an ongoing basis? Then write SQL that calculates, over the last 30 days, each carrier's total shipments, total defective shipments, and defect rate (defective / total). Return only the top 3 carriers by defect rate.

Product Analytics & MetricsData Modeling

Author's notes

The metrics part I actually liked.

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AI HintsAI Generated

Suggested Approach

Start by defining a balanced set of quality metrics that cover defect frequency, severity, and trends over time. Then, for the SQL portion, clarify assumptions about the data model and write a query that aggregates shipments and defects by carrier over the last 30 days, calculates defect rates, and returns the top 3 carriers with the highest rates.

Pro tip: In the metrics discussion, emphasize actionable metrics that tie to business outcomes, and in SQL, explicitly state your assumptions about the schema and date filtering to show attention to detail.

1. Define Core Quality Metrics

Identify key metrics such as defect rate, defect count, and total shipments, ensuring they are measurable and aligned with business goals.

2. Add Granularity and Trends

Break down metrics by dimensions like carrier, product category, and time period to enable trend analysis and root cause investigation.

3. Incorporate Severity and Impact

Include metrics that capture defect severity (e.g., cost of defects, customer impact) to prioritize quality improvements.

4. Write SQL for Carrier Defect Rates

Assume a schema with shipments and defects tables; write a query that filters shipments from the last 30 days, aggregates by carrier, computes defect rate, and returns the top 3 carriers by defect rate.

5. Validate and Interpret Results

Check for data quality issues (e.g., missing dates, duplicate records) and discuss how to interpret the results for operational improvements.

Key Points to Mention

Defect rate as a primary metric, with numerator (defective shipments) and denominator (total shipments) clearly defined.
Time-based trends (e.g., daily/weekly defect rates) to detect anomalies and monitor improvements.
Segmentation by carrier, region, and product type to identify specific problem areas.
Severity-weighted metrics (e.g., cost per defect) to prioritize high-impact issues.
SQL considerations: date filtering (e.g., using CURRENT_DATE - INTERVAL '30 days'), handling NULLs, and ensuring accurate joins between shipments and defects.
Business context: how these metrics drive carrier performance reviews and quality initiatives.

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