Epic Games Data Scientist interview typically runs 3 rounds: recruiter conversation, hiring manager, and an 8-person panel. The process took about a month longer than expected, with a rigid, choppy panel format.
$139K
Avg. Base Comp
$153K
Avg. Total Comp
3-4
Typical Rounds
4-6 weeks
Process Length
Our candidates report that Epic Games cares less about polished theory and more about whether you can defend real work you personally owned. The clearest signal in the experience we saw was the deep dive into a challenging experiment: not “what’s the right answer,” but how the setup was handled, what tradeoffs were made, and why the work was difficult in practice. That tells us the bar is anchored in applied judgment, especially for data scientists supporting a business team where decisions need to be credible, not just statistically neat.
A recurring theme is that the process can feel more rigid than candidates expect. One candidate described the panel as choppy and highly question-driven, with multiple interviewers making sure they each had room to probe. That usually means Epic is looking for people who can stay crisp under interruption and keep their story consistent across different stakeholders. We’ve also seen signs that fit and background matter a lot: the hiring manager and recruiter both changed mid-process, and the candidate never got a clean one-on-one with the final manager before the panel. In practice, that puts extra weight on how convincingly you explain your past decisions, because the team is piecing together confidence from several conversations rather than one smooth narrative.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Epic Games process.
Pretty straightforward process overall. I started with a recruiter conversation, then spoke with the hiring manager, and finished with an 8-person panel made up of people from the business area I’d support plus peers. The whole thing stretched longer than I expected because Epic has an extended holiday break and doesn’t really interview in the week before or after it, so my timeline got pushed out by about a month. That wasn’t ideal from a candidate perspective, but it also made sense once I understood how they operate.
The most memorable part was that the hiring manager and recruiter both changed during the process. The first hiring manager conversation felt a little like a placeholder since I wasn’t sure how much weight it would carry, and I never got a separate one-on-one with the new hiring manager, though he was on the panel. The panel itself was more rigid than I expected. I was told it would be conversational, but with multiple 2-on-1 interviews back to back, it felt like everyone was trying to make sure they had time to ask their own questions, so the flow was pretty choppy.
On the technical side, the main question I remember was about an experiment I had run that was particularly challenging. It was less about textbook theory and more about how I handled the setup, the tradeoffs, and what made it difficult in practice. That fit the overall vibe of the interview, which seemed focused on whether I’d actually done the work and could talk through it clearly. After the panel, it took about two weeks to hear back. My recruiter was responsive during that wait, but the rejection email came through the ATS and felt a bit impersonal after such a long process. I also asked for feedback and didn’t get a response. My takeaway is that Epic seems very selective about fit and background, so it helps to be ready to speak in detail about a real experiment you owned and the decisions behind it.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Epic Games
Write a function to rotate an array by 90 degrees in the clockwise direction.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Compute Deviation | |
| Button AB Test | |
| P-value to a Layman | |
| WAU vs Open Rates | |
| Integer to Roman | |
| Google Maps Improvement | |
| Group Success | |
| Significance Time Series | |
| Compute Variance | |
| Distribution of 2X - Y | |
| Scalped Ticket | |
| Nearest Common Ancestor | |
| Subscription Retention | |
| Marketing Channel Metrics | |
| Time on FB Distribution | |
| Comparing Search Engines | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| Tower of Hanoi | |
| Simulating Coin Tosses | |
| Spam Classifier | |
| Customer Success vs. Free Trial | |
| Track Your Most Valuable Gamers | |
| New UI Effect | |
| KNN From Scratch | |
| Bootstrapping Confidence Intervals | |
| Interquartile Distance | |
| Implementing the Fibonacci Sequence in Three Different Methods |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with an initial conversation with a recruiter to discuss your background, the role, and overall fit. In this case, the recruiter later changed during the process, but the recruiter screen was still the first formal step.
Next, candidates speak with the hiring manager about their experience and how they would support the business area. The interview felt somewhat like a placeholder to the candidate, and in this case the hiring manager changed later in the process.
The final stage was an 8-person panel made up of peers and people from the business area the candidate would support. It was more rigid than expected, with multiple back-to-back 2-on-1 interviews, and included detailed questions about real experiments the candidate had run, especially challenging ones and the tradeoffs involved.
After the panel, the candidate heard back in about two weeks. The process ended with a rejection communicated through the ATS, and no additional feedback was provided.