Investigate Causes of Increased Payroll Processing Time
Overview
This question evaluates a data scientist's diagnostic analytics, causal reasoning, and experimentation skills for operational performance issues in payroll systems, including hypothesis framing, metric selection, segmentation, and validation.
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:
- Form hypotheses about likely root causes.
- Identify required datasets and key metrics.
- Define useful cuts/segments (e.g., time, geography, product tier, company size).
- Sequence the investigation to efficiently localize the issue.
- 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).
Solution
Login required