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Instacart·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Instacart data science interview with a single meaty case question about diagnosing a revenue drop using only a weekly time series. No behavioral rounds that I can recall, just this one scenario that spiraled into a broader conversation about what you'd do with more data.

Questions Asked (1)

Q1

Instacart's weekly revenue dropped 4% compared to the prior week and you only have the historical weekly revenue time series. How do you investigate the decline? And if you later got access to richer data like orders, geography, and average order value, what would you do next?

Root Cause AnalysisProduct Analytics & MetricsAdaptability & Ambiguity
Author's notes

This one is deceptively open-ended.

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AI HintsAI Generated

Suggested Approach

Start by validating the data and decomposing the 4% drop using the limited time series (trend, seasonality, anomalies). Then, when richer data is available, systematically drill down by dimensions like orders, geography, and AOV to isolate the root cause and quantify each driver's impact.

Pro tip: Always quantify the contribution of each factor to the overall decline (e.g., a 4% drop could be 3% from fewer orders and 1% from lower AOV) to prioritize actions and communicate impact clearly.

1. Validate and contextualize the data

Check for data quality issues (missing days, outliers) and compare the 4% drop to historical variability (e.g., typical week-over-week changes, seasonality). Determine if the drop is statistically significant or within normal fluctuation.

2. Decompose the time series

Break down the revenue series into trend, seasonal, and residual components to see if the drop is due to a shift in trend, an unusual seasonal pattern, or a one-off event. Look for changepoints or anomalies.

3. Form hypotheses and segment with richer data

With orders, geography, and AOV, segment the decline by dimensions: e.g., by region, customer cohort, product category, or order channel. Calculate each segment's contribution to the total drop.

4. Quantify drivers and identify root cause

Use decomposition (e.g., revenue = orders × AOV) to quantify how much of the 4% drop is due to fewer orders vs. lower AOV. Further drill into orders by geography or customer type to pinpoint the source.

5. Synthesize findings and recommend actions

Summarize the key drivers, estimate their impact, and suggest next steps (e.g., investigate a specific region's delivery issues, test a promotion). Prioritize based on potential impact and feasibility.

Key Points to Mention

  • Check for data quality and seasonality before assuming a real decline.
  • Use decomposition: revenue = orders × average order value (AOV).
  • Segment by geography, customer cohorts, and time to isolate the drop.
  • Quantify each driver's contribution to the overall 4% decline.
  • Consider external factors (holidays, weather, competitors) and internal factors (app changes, promotions).
  • Recommend follow-up analyses or experiments to confirm root cause.

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