Investigate Causes of Increased Payroll Processing Time
Diagnose a 15% Increase in Payroll Processing Time
Scenario
You are a data scientist at a payroll platform. The average monthly payroll processing time increased by 15% compared with the previous month. Assume "processing time" is the elapsed time from when an admin clicks "Run payroll" to when the system confirms the run is complete (including validations, tax/benefit calculations, payments file submission, and confirmations).
Task
Using data, lay out how you would:
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Form hypotheses about likely root causes.
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Identify required datasets and key metrics.
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Define useful cuts/segments (e.g., time, geography, product tier, company size).
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Sequence the investigation to efficiently localize the issue.
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Conclude with recommended next steps, including potential experiments and fixes.
State any assumptions and explain how you would distinguish mix-shift effects from true slowdowns. Include how you would validate that the 15% increase is real (not due to seasonality, holidays, or partial-month artifacts).
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
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State assumptions about instrumentation, randomization, sample size, and data quality.
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Separate descriptive analysis from causal claims.
What a Strong Answer Covers
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A metric framework with primary, guardrail, and diagnostic metrics.
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A credible analysis or experiment design with clear assumptions and bias checks.
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SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
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An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions
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What sanity checks would you run before trusting the result?
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How would you handle novelty effects, seasonality, or selection bias?
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What decision would you make if metrics disagree?