Quant Interview Prep for Non-Finance Majors
Quant interview prep priorities for non-finance majors: probability, statistics, markets vocabulary, projects, and interview practice.
STEM students and career switchers without formal finance coursework.
Finance background is not the whole prep
Most quant interviews weight probability, statistics, coding, and reasoning far above formal finance coursework. A first-round trading interview is much more likely to ask you to price a dice game than to define WACC. You do need working market vocabulary, but a missing finance degree is rarely the gap that actually costs candidates the offer - the ones who struggle are not the people who skipped corporate finance, they are the people whose sample-space discipline is shaky.
Prioritize transferable foundations
Start where the interview actually spends its time: sample-space setup and counting, conditional probability and Bayes, expected value and linearity, variance and covariance intuition, mental arithmetic, and coding where the role calls for it. These transfer across trading, research, developer, and internship interviews with almost no rework, which makes them the highest-return hours available to you.
Learn markets vocabulary through use
The working vocabulary is short, and you learn it faster by using it than by reading definitions. Bid is what you will buy at, ask is what you will sell at, and the spread between them is your compensation for uncertainty. Fair value is your midpoint estimate. Adverse selection is the fact that the people who choose to trade with you are disproportionately the ones who know more. Inventory is the position you have accumulated and the risk it carries. Liquidity is how much you can trade without moving the price. Six terms, used inside market making practice, will take you further than a semester of definitions you cannot apply.
Concrete example
The gap differs by background, and naming yours saves weeks. A physics major usually arrives with strong estimation and modeling habits but has never set a two-sided market, so the work is trading games and inventory. A computer science major often has the coding and the complexity reasoning but has not touched expected value or conditional probability since one undergraduate course. A pure math major typically has all the machinery and needs communication reps - abstract reasoning spoken aloud at interview pace, with the assumptions stated. Diagnose which of these you are before you choose a study plan.
Use projects honestly
A project buys you a technical conversation, so it has to survive one. A small order-book simulator, a clean analysis of a public dataset, or a toy strategy whose limitations you can state precisely all work well. A dramatic backtest claim does not, because the first follow-up will be about transaction costs, lookahead bias, or sample size - and "I am not sure" after a bold claim lands worse than never having made it. Prefer a modest project you understand completely to an impressive one you do not.
Common mistakes
The characteristic mistake is overcorrecting: trying to learn all of finance before practicing a single interview problem, which spends your scarce weeks on the least-tested material. The mirror-image mistake is refusing to learn any vocabulary at all and then being unable to say why a market should be wide. Learn the six terms, then spend everything else on the mathematics and the explanation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.