Flowhub Data Scientist Interview Guide

The most frustrating part of my Flowhub Data Scientist interview was not the difficulty so much as the timing. The process stretched to roughly two to three months because my behavioral interview was moved back by about a month. I ultimately did not receive an offer.

The first substantive round was a 45-minute interview combining SQL with a product-sense problem. The SQL portion involved writing a query to filter data, so it was fairly direct, but I also had to think through how I would define and evaluate a product rather than just produce code. One representative prompt was how I would measure the health of a Facebook page, which tested whether I could choose sensible metrics and explain what they indicated. Another technical area that came up in the process was hyperparameter tuning alongside SQL coding.

The onsite consisted of four 45-minute rounds covering behavioral questions, AR, AE, and another SQL interview. The behavioral discussion focused on my experience and what I wanted from my next role. The technical interviews required more breadth: SQL was central, but I also encountered methods-style coding questions involving linked lists and dynamic programming. Those questions were notably more algorithmic than the filtering query, with a mix of medium- and hard-level difficulty. That variation was worth noting because the process was not limited to analytics or product thinking; it could shift into traditional coding problems.

The people I spoke with were friendly, and the role and team structure were explained clearly. My main advice is to prepare for a broad process rather than treating this as a SQL-only data science interview. Be ready to move between query writing, product-health metrics, model tuning, behavioral discussion, and algorithmic coding, and expect scheduling to make the overall process longer than the individual rounds suggest.