Data Cleaning Quant Interview Guide
Data cleaning quant interview guide for missing data, outliers, timestamps, joins, validation, leakage, examples, and mistakes.
Candidates working with messy research or market data.
Cleaning choices are modeling choices
Handling missing values, outliers, duplicates, and timestamps changes the dataset. In quant interviews, explain why each cleaning choice is appropriate.
Timestamps deserve special care
Market and research data often has release times, revisions, and time zones. A clean-looking dataset can still leak future information.
Concrete example
Dropping all rows with missing returns may bias a sample if missingness is related to trading halts, illiquidity, or data vendor coverage.
Validate every join
Check key uniqueness, row counts, unmatched rows, and whether the joined data was available at the decision time. These checks catch silent data errors.
Common mistakes
Candidates often delete inconvenient observations without explaining the bias risk. A stronger answer says what was removed and how that could affect results.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.