Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Amazon data scientist interview with a case-style question about diagnosing a business decline. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

If a key business metric suddenly drops, how would you go about diagnosing the root cause using data? Walk through the metrics you'd look at and where you'd get that data.

Root Cause AnalysisProduct Analytics & MetricsA/B Testing & Experimentation

Author's notes

I started with conversion funnel breakdowns and felt okay about that, but I fumbled when they pushed on external factors.

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AI HintsAI Generated

Suggested Approach

Start by validating the metric drop and checking for data pipeline issues, then segment the metric by dimensions to isolate the affected population. Form hypotheses about potential causes and test them using appropriate data sources and statistical methods.

Pro tip: Always consider seasonality and external factors (e.g., holidays, competitor actions) before assuming an internal issue; this shows you think holistically and avoid false alarms.

1. Validate the metric and data quality

Confirm the drop is real by checking data pipeline health, ETL jobs, and logging. Ensure there are no instrumentation errors or missing data.

2. Segment and localize the drop

Break down the metric by dimensions such as time, geography, platform, user cohort, and product category to identify where the drop is concentrated.

3. Form and test hypotheses

Generate hypotheses about potential causes (e.g., feature release, marketing campaign, pricing change) and test them using A/B test results, logs, or external data.

4. Quantify impact and identify root cause

Use statistical methods like regression or causal inference to quantify the impact of each factor and isolate the primary driver.

5. Recommend and monitor actions

Propose remediation steps based on findings and set up monitoring to track recovery and prevent future occurrences.

Key Points to Mention

Data sources: internal databases (e.g., Redshift, DynamoDB), logging systems (e.g., CloudWatch), A/B testing platforms, and external data (e.g., market trends).
Segmentation dimensions: time (hourly, daily), user demographics, device type, geographic region, traffic source, and product features.
Statistical techniques: hypothesis testing, anomaly detection, regression analysis, and causal inference methods like difference-in-differences.
Cross-functional collaboration: work with engineering, product, and marketing teams to gather context and validate hypotheses.
Amazon-specific tools and metrics: familiarity with Amazon's data infrastructure (e.g., AWS services) and key business metrics (e.g., conversion rate, revenue, customer engagement).
Communication: clearly articulate findings and recommendations to stakeholders, emphasizing data-driven decision making.

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