Fetch Rewards, Inc. Data Scientist Interview Guide

We've seen Fetch Rewards lean hard into applied problem-solving over polished theory. In the candidate experience we reviewed, the early technical discussion centered on SQL and an A/B testing scenario, and the later conversation mixed SQL/Python coding, statistics, a case study, and a hiring manager discussion. That combination tells us the team wants people who can move comfortably from data manipulation to product thinking without losing precision. For a consumer app like Fetch, that usually means they care less about textbook definitions and more about whether you can reason through messy, real-world data and explain your choices clearly.

A recurring theme is that the bar can feel uneven depending on who is in the room. One candidate noted that the coding interviewers seemed too junior to fully follow their Python code, which created friction even when the solution itself was sound. That’s an important signal: at Fetch, clarity of communication matters as much as correctness, especially when your work may need to be defended to non-experts. We’d also pay attention to the duplicate-receipts prompt, which points to the kind of data quality and entity-matching thinking that shows up in retail and receipt-based products. Candidates who do best here tend to make their assumptions explicit, stay structured, and show they can handle ambiguous product data without overcomplicating the answer.