EY Quantitative Analyst candidates report assessments and interviews spanning quantitative fundamentals, coding, model validation, resume discussion, and behavioral or case-based judgment.
$126K
Avg. Base Comp
$146K
Avg. Total Comp
2 rounds
Typical Rounds
2-4 weeks
Process Length
EY Quantitative Analyst interviews can vary sharply: some candidates reported an assessment followed by technical conversations, while another described an online interview series with behavioral interviews and case work. One candidate explicitly reported two entirely technical rounds, so treat the reported format as a range of paths rather than a fixed sequence.
Technical preparation should start with your own work. Candidates were asked to explain model choice, data access and manipulation, parameter or coefficient meaning, and backtesting. A separate report focused on assessing a strategy’s robustness, choosing performance metrics, and recognizing overfitting. Be ready to reason aloud about probability and statistics, including correlation, and to discuss option pricing concepts such as barrier options where relevant.
Coding and data fundamentals also appear in the reports. Candidates mentioned Python or C++ coding, SQL basics, algorithmic questions, and a Python question on mutable versus immutable types. Resume follow-ups were common, so be able to connect projects, academic work, and certifications to concrete decisions you made.
The nontechnical side may test practical judgment as well. Reported prompts included process optimization, handling an emergency, collaboration across different leadership styles, strengths and weaknesses, and a live business case. Prepare concise examples that explain the situation, your approach, and the result.
Synthesized from 8 candidate reports by our editorial team.
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Real interview reports from people who went through the Ey process.
The EY interview for the Quantitative Analyst role was pretty structured and leaned more technical than I expected. It began with an HR screening that was mostly about my background and why I was interested in the role, and then moved into technical interviews. The technical part was less about memorizing formulas and more about how I think through quantitative problems in practice. I was asked probability and statistics questions, along with some modeling concepts, but the most memorable part was the discussion around evaluating a strategy and interpreting backtesting results.
That backtest conversation was the hardest part for me because they wanted to know how I would judge whether a model was actually robust, how I’d think about overfitting, and which metrics I’d use when reviewing performance. It felt like they were testing whether I could move from theory to real-world judgment, not just solve textbook problems. Overall the difficulty was moderate to fairly technical, especially if you’re not used to explaining model validation out loud. I didn’t get an offer, but the process was clear and professional. If you’re preparing for EY, I’d focus on being able to talk through backtest evaluation, robustness checks, and how you’d spot overfitting in a strategy rather than only drilling pure probability questions.
Prep tip from this candidate
Be ready to explain how you would evaluate a trading strategy or model in practice, especially what metrics you’d inspect in a backtest and how you’d detect overfitting. Also review probability and statistics concepts, since those came up alongside the strategy-validation discussion.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report recruiter or HR screens and, in several paths, an analytical or online assessment before later interviews. Reported assessments included logical reasoning, coding challenges, and probability or statistics questions; some candidates instead described a background-focused early conversation.
Candidates report technical conversations on probability, statistics, algorithms, Python, SQL, option pricing, and resume projects. Discussions may probe how you selected a model, interpret coefficients, backtest results, evaluate robustness, and identify overfitting rather than only ask for definitions.
Some candidates report behavioral interviews or a live business case after technical evaluation. Prompts included working with a different leadership style, optimizing a process, responding to an emergency, strengths and weaknesses, and explaining how you work through an open-ended problem with colleagues.
