Alternative Data Cleaning Interview Guide
Alternative data cleaning interview guide covering identifiers, duplicates, missingness, revisions, outliers, units, and validation.
Candidates handling messy, vendor-supplied, or unstructured research data.
Cleaning starts with identity
Alternative data often needs entity mapping across tickers, stores, brands, geographies, devices, documents, or vendor-specific identifiers.
Missingness can be informative
Missing data may reflect coverage limits, vendor outages, reporting changes, or real-world behavior, so it should not be blindly filled.
Concrete example
A store-traffic feed with missing locations may bias a retail signal if high-performing or low-performing regions are absent.
Validate transformations
Check duplicates, units, outliers, timestamp consistency, revisions, coverage drift, and whether cleaning rules use future information.
Common mistakes
Candidates often jump to model features. A strong answer explains how raw vendor data becomes trustworthy research input.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.