Fetch Rewards·Data Scientist·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Fetch Rewards data scientist interview with a meaty open-ended case study about validating a business claim using a single transactions table under time pressure. The question was layered enough that I kept second-guessing whether I was going deep enough or just spinning my wheels.

Questions Asked (1)

A beverage company says Q2 sales dropped. You have only a transactions table and 45 minutes. Walk through how you'd rigorously validate or refute that claim, covering metric choice, analysis grain, step-by-step plan, sanity checks, outputs, and what additional data you'd request if you had more time.

Product Analytics & MetricsRoot Cause AnalysisData Modeling

Author's notes

This one is basically a full case study compressed into a single prompt.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric definition and analysis grain, then outline a step-by-step validation plan that includes sanity checks and outputs. Emphasize the need for additional data to rigorously confirm or refute the claim, and structure your answer to show analytical rigor within the 45-minute constraint.

Pro tip: Always validate the data quality and metric definition before diving into analysis; a quick sanity check can save you from presenting misleading insights.

1. Clarify Metric and Grain

Define what 'sales' means (e.g., revenue, units, transactions) and determine the appropriate analysis grain (e.g., daily, weekly, per store, per product).

2. Validate Data Quality

Check for missing values, duplicates, outliers, and time zone issues. Ensure the transactions table is complete and accurate for Q2 and comparison periods.

3. Conduct Trend and Segment Analysis

Compare Q2 sales to previous quarters and same quarter last year. Break down by key dimensions (e.g., product, region, channel) to identify where declines occur.

4. Perform Sanity Checks and Statistical Tests

Verify that any drop is not due to seasonality, data artifacts, or random variation. Use confidence intervals or hypothesis testing if possible.

5. Summarize Findings and Request Additional Data

Present clear outputs (e.g., charts, summary tables) and list additional data needed (e.g., marketing spend, competitor activity, economic indicators) to confirm root cause.

Key Points to Mention

Metric definition: revenue vs. units, gross vs. net sales
Analysis grain: time (daily/weekly), entity (store, product, customer)
Data quality checks: missing data, outliers, duplicates
Comparison periods: QoQ, YoY, and seasonality adjustment
Segmentation: by product, region, channel, customer segment
Additional data: marketing campaigns, pricing changes, competitor actions, economic factors

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