EY Data Scientist interview typically runs 3 rounds: a panel technical interview, a senior manager conversation, and a partner discussion. The process takes a few weeks and is distinguished by heavy project defense over coding challenges.
$136K
Avg. Base Comp
$143K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
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.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Ey process.
The first round was the most intense one for me because it had three panelists at once, covering data science, SQL, Power BI, and HR. A lot of it circled around the projects on my resume, especially work I had done in LLMs, NLP, and Python. They kept pushing with follow-up questions like what would happen if I used one approach instead of another, so it felt less like memorizing answers and more like defending the choices I made. They also asked about topics like RAG, RCNN, SQL, and Power BI data modeling, so I had to switch between project discussion and technical depth pretty quickly. The second round was with a senior manager and was much more conversational. That interviewer focused on my personal experience, how I worked with teams, and general team-management style questions, plus a discussion of the projects the team was working on. The last call was with a partner, and that was the part I found most surprising. I expected a standard HR-style conversation, but the partner asked me to explain every project first and then moved into HR questions after that. Overall the process felt more like a mix of project defense and fit discussion than a pure coding interview. I did not get the offer, and the whole thing felt a bit tougher than I expected from the way the later rounds were framed.
Prep tip from this candidate
Be ready to walk through every project on your resume in detail, especially anything involving LLMs, NLP, RAG, or Python, and expect follow-up questions that challenge your design choices. Also prepare for SQL and Power BI data modeling questions in the same interview rather than as separate technical rounds.
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Topics based on recent interview experiences.
Featured question at Ey
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Sort Strings | |
| Hurdles In Data Projects | |
| Forecasting New Year Revenue | |
| Classification and Regression | |
| Overfit Avoidance | |
| Stakeholder Communication | |
| Simple Explanations | |
| Why Do You Want to Work With Us | |
| Xgboost vs Random Forest | |
| Your Strengths and Weaknesses | |
| 1000 Sample Classifier | |
| Marketing Dollar Efficiency | |
| Quantify Uncertainty | |
| Linear vs Logistic Regression | |
| Backpropagation Explanation | |
| Experiment Validity | |
| Raining in Seattle | |
| Bagging vs Boosting | |
| Revenue Retention | |
| P-value to a Layman | |
| Using R Squared | |
| Cyclic Detection | |
| Assumptions of Linear Regression | |
| Precision and Recall | |
| Missing Housing Data | |
| Find Duplicate Numbers in a List | |
| Data Preparation for Imbalanced Data | |
| Spam Classifier | |
| Bias vs. Variance Tradeoff |
Synthesized from candidate reports. Individual experiences may vary.
A multi-panelist round with three interviewers covering data science, SQL, Power BI, and HR topics simultaneously. Candidates are expected to defend project choices in depth, including work on LLMs, NLP, Python, RAG, and RCNN, with follow-up questions probing alternative approaches and technical trade-offs.
A conversational round with a senior manager focused on personal experience, cross-functional collaboration, and team management style. The interviewer also discusses ongoing team projects to assess cultural and operational fit.
A final round with a partner that begins with a walkthrough of the candidate's projects before transitioning into behavioral and HR-style questions. Despite appearing conversational, this round includes substantive project discussion and is more rigorous than a standard fit interview.