Quant interview preparation

Prop market making and quantitative research, weighted the way the interviews actually are: probability and expected value, statistics and machine learning, market making logic, programming and options. Every question is either traced to a named firm from a public candidate report, or tagged at desk level when we could not trace it, and every probability answer shows the reasoning path rather than just the number.

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Question bank

100 questions, mapped to the firms that asked them

Questions
100
Traced to a firm
53
Firms
15
Updated
September 2026
Asked at
All firmsOld Mission Capital12Tower Research Capital10Jump Trading7Akuna Capital5Citadel4D.E. Shaw3Jane Street3AQR Capital Management2DRW2Millennium Management2Schonfeld2Squarepoint Capital2Susquehanna International Group2Belvedere Trading1Optiver1
Topic
All topicsProbability10Coins, cards and games6Expected value8Statistics11Market making15Estimation and mental maths4Stochastic processes4Regression5Machine learning6Time series6Programming10Options and derivatives8Fit and motivation7
Level
AnyCoreIntermediateHard
Type
AnyBrainteaserTechnicalCaseMarket viewFit
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  1. 097Describe a technical challenge you solved, so that a non-expert understands it but an expert still learns something.Fit and motivationHardsuperdayAkuna CapitalTrading · Chicago · 2026

    Say this

    Structure it in three layers. One sentence on the problem in plain language that anyone would care about, then the mechanism in an analogy with no jargon, then one specific technical detail that a specialist would find non-obvious. The last layer is what the question is really asking for.

    Then walk it

    1. Layer one, the stakes in plain words: our pipeline took six hours and we needed it in twenty minutes, or the model was accurate in testing and wrong in production and nobody knew why.
    2. Layer two, the mechanism by analogy. Pick an analogy that is load-bearing rather than decorative. For a cache: it is like keeping the twenty files you use on your desk instead of walking to the archive each time, and the hard part is deciding what to throw off the desk.
    3. Layer three, the detail for the expert. One precise, surprising thing. The bottleneck was not compute, it was that we were re-parsing timestamps sixty million times, and interning them cut the runtime by eighty percent. Or: the bug was that the validation split was random rather than chronological, so the model was reading the future.
    4. Say what you rejected and why. That is where an expert learns something, because it shows the decision space and not just the destination.
    5. And control the length. Ninety seconds, then stop and let them ask. The hardest part of this question is not the content, it is the discipline to stop talking, and a candidate who tests understanding by pausing rather than narrating for five minutes has already demonstrated the skill being measured.

    Where candidates lose it

    Picking either the audience-friendly version or the expert version and doing only one. The question explicitly demands both layers. Also do not choose your most complicated project, choose the one where you can name one genuinely surprising detail, because the surprise is what makes an expert lean in.

    Expect next

    • What was the alternative you rejected, and why?
    • How did you know your fix actually worked?
    • Explain it again, but in thirty seconds.

    Reported by candidates at Akuna Capital (Trading, Chicago, 2026). Source: Wall Street Oasis.

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.