Ey Data Scientist Interview Guide

What stands out most about EY's data science interview process is how heavily it leans on project defense rather than abstract problem-solving. We've seen this pattern clearly: the technical panel isn't just checking whether you know what RAG or RCNN is — they want to know why you made the choices you did and what would have happened if you'd gone a different direction. That kind of adversarial follow-up is easy to underestimate if you walk in expecting a standard Q&A format. Candidates who treat their resume projects as conversation starters rather than rehearsed talking points tend to fare much better.

Another non-obvious dynamic here is the range of tools in play. SQL, Power BI, and advanced ML concepts like LLMs and NLP can all surface in the same panel conversation, sometimes within minutes of each other. The ability to context-switch quickly — from data modeling to model architecture to business framing — is something EY seems to value highly, which makes sense given that their data scientists often sit close to client-facing advisory work.

The partner round is where candidates consistently get caught off guard. Our one reported experience confirms what we'd expect from a consulting firm: even the most senior conversation starts with a full project walkthrough before moving into fit questions. EY appears to want partners to form their own technical impression rather than rely solely on earlier rounds. That means you should never assume a late-stage conversation is purely cultural — every round at EY is still a technical audition in some form.