Roku Inc. Data Scientist Interview Guide

We’ve seen Roku care less about abstract machine learning theory and more about whether candidates can explain a real project end to end with enough specificity to show judgment. In the candidate experience we reviewed, the strongest signal wasn’t a clever model choice; it was the ability to walk through the problem, the data, the model, and the outcome in a way that made tradeoffs obvious. That tells us Roku is listening for depth, not polish. If a candidate can’t clearly defend why they chose a particular approach or what changed after the results came back, the conversation tends to stall.

A recurring theme is Roku’s emphasis on the business side of the role, especially the Roku product and advertising ecosystem. Multiple candidates reported being pushed on whether they understood how their work would connect to the ad business, and whether they could speak the language of the team rather than just the language of modeling. We also see a strong preference for people who can collaborate cleanly and discuss team-relevant concepts without sounding rehearsed. The non-obvious make-or-break here is not raw technical breadth; it’s whether your past ML work feels transferable to Roku’s product reality. Candidates who can tie their decisions to business impact seem to land much better than those who stay at the level of generic data science.