Unstructured Data Quant Interview Guide
Unstructured data quant interview guide covering text, images, extraction, labels, noise, compliance, validation, and examples.
Candidates discussing text, documents, images, or alternative datasets.
Unstructured data needs extraction
Text, images, audio, filings, and documents must be transformed into structured features before most quant research workflows can use them.
Labels and timing are fragile
Extraction quality, model drift, publication time, document revisions, and entity matching can all create noise or leakage.
Concrete example
A news headline feature should use article timestamp, source reliability, duplicate handling, and entity mapping before testing return prediction.
Compliance matters
Data rights, licensing, privacy, and acceptable use are part of the research process; do not assume every available document is usable.
Common mistakes
Candidates often treat NLP output as a clean numeric signal. Extraction error and coverage bias must be validated out of sample.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.