The Home Depot Data Scientist Interview Guide

We've seen a clear pattern in The Home Depot's interviews: they respond best when candidates treat the problem like a real business dataset, not a canned machine-learning prompt. In both experiences, the strongest signal was starting with the data itself — one candidate won over the panel by walking through EDA in Colab, noticing the distribution looked log-normal, and explaining why that changed the approach. That kind of observation matters here because the team seems to value practical judgment over flashy model selection.

A recurring theme is that the interviewers are comfortable with open-ended ambiguity, but they want to see whether you can reason through it out loud. The outlier-detection prompt was framed loosely as telemetry or sensor data, and the candidate who did well did not rush to a complex sklearn solution. Instead, they proposed a simple percentile threshold first, then refined the idea based on what the data suggested. That tells us the bar is less about naming the most advanced method and more about making defensible choices from imperfect information.

We also see a collaborative tone across candidate reports: the best conversations turned into genuine technical back-and-forth with a Staff Engineer or DS who clearly appreciated nuance. For candidates, the non-obvious make-or-break is whether you can articulate why a method fits the shape of the data and the use case. At Home Depot, that kind of grounded, data-first thinking seems to carry more weight than trying to impress with a prepackaged answer.