BioCatch Data Analyst Interview Guide

Our candidates’ experience suggests BioCatch is less interested in polished textbook answers and more interested in whether you can reason through messy product data like an investigator. The standout theme is source reconciliation: one candidate was asked to compare fields coming from a user-agent feed versus a second, unnamed source and explain which values should be trusted. That tells us the team cares about whether you can spot conflicting definitions, understand where a field originates, and make a defensible call when two systems disagree.

A second pattern is the emphasis on nulls and column-level dependencies. Rather than treating missingness as a generic data-quality issue, BioCatch seems to want analysts who can trace why nulls appear and which upstream attributes influence them. In a fraud and behavioral-biometrics context, that matters: if you can’t explain missing device or platform data, you can’t reliably interpret model inputs or downstream risk signals. We’d expect strong candidates to talk clearly about data lineage, schema semantics, and the difference between raw event data and derived or enriched attributes.

We also notice that the technical conversation was framed around a real table and a Python-based analysis, which suggests BioCatch values practical debugging over abstract theory. The best signal here is not just getting the “right” answer, but showing a structured way to inspect the dataset, identify inconsistencies, and communicate uncertainty without overclaiming. That combination of rigor and judgment is what tends to separate solid analysts from people who only know how to summarize data.