IntermediatePROBLEM_SOLVING
Tell me about a time when the dataset you were working with was incomplete, noisy, or inconsistent. How did you detect the issues, what specific cleaning techniques did you apply, and how did you validate that the cleaned data was reliable?
Custom Role
General

Sample Answer

On a churn prediction project for about 250k subscription users, the initial dataset was pretty messy. I noticed right away that simple aggregates didn’t line up: active users in the event logs were 15% higher than in our billing system, and around 12% of rows had impossible timestamps (events logged after cancellation). I started with systematic data profiling: null rates, distinct counts by key, cross-table checks, and time-series sanity checks. I fixed issues in layers. First, I standardized IDs and joined against a “source of truth” customer master table, which resolved most duplicates and orphans. Then I applied outlier detection on numeric fields (z-scores and IQR) and capped or excluded clear logging errors. For missing values, I used domain-driven rules (e.g., “no event for 90 days” = inactive) and simple model-based imputation for a few continuous fields. To validate, I re-ran reconciliation against finance reports, did before/after KPI checks, and held back a raw sample to confirm that model lift (AUC) improved from 0.68 to 0.76 without weird behavioral shifts.

Keywords

Used data profiling and cross-system reconciliation to detect inconsistenciesStandardized keys and used a master data source to resolve duplicates and orphansApplied outlier handling and targeted imputation based on domain rulesValidated cleaning via KPI checks, reconciliation to finance, and model performance lift
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