Jane Street quant interview preparation: a role-specific study guide

Prepare collaborative probability reasoning, conditional value and clear communication for a Jane Street application. Start with the firm’s own role-specific interview guidance, then use the original practice examples below. These are SelfMadeQuant teaching questions, not reported Jane Street questions; the study plan is editorial and does not promise a particular assessment format.

What the official guidance supports

Jane Street describes collaborative problem solving and provides separate information for quantitative trading, research and technology. Its trading guidance says that prior finance knowledge is not tested. Use the official interviewing page to select your discipline, then read the linked preparation materials. Those statements are specific to the described roles; they should not be generalised to every vacancy.

Trading preparation: make your conditioning audible

Start with expectation, conditional probability and the value of new information. When asked for a fair price, define the payoff and what is known before calculating. Explain why a new observation changes the sample space. A collaborator should be able to follow your reasoning without guessing which assumptions you silently introduced.

Practise changing an answer when the experiment changes. If a die is biased, if a card is selected by a different mechanism, or if an opponent knows more than you, a memorised solution may no longer apply. In a quoting exercise, record the information implied by a fill as well as its effect on inventory.

Research and software preparation

For research, prepare one project deeply enough to explain the hypothesis, the data boundary, a failed experiment and a limitation. For software, practise implementing a small specification, explaining complexity and testing awkward inputs. Consult the official discipline-specific guidance rather than substituting trading puzzles for a coding interview.

A focused practice session

Work the three examples below aloud, including a boundary check. Then change one input and solve the variant without looking at the original answer. Finish by explaining a technical project in five minutes, followed by questions from a partner. Score your clarity, assumptions and ability to respond to correction; do not treat a practice score as a hiring forecast.

Original practice questions with worked answers

1. A value before and after information

A contract pays the sum of two independent fair six-sided dice. What is its expected value before either reveal, and after the first die is revealed as 5?

Answer. Before the reveal, the mean is 3.5 + 3.5 = 7. After observing 5, the conditional mean is 5 + 3.5 = 8.5. The unrevealed die remains fair and independent.

2. A fill is an observation

A hidden value is uniformly one of 4, 5, 6 or 7. An informed buyer accepts your ask of 5.5 exactly when the value exceeds the ask. What is your conditional expected loss per sale?

Answer. Acceptance restricts the value to 6 or 7, with conditional mean 6.5. You receive 5.5 for an expected value of 6.5, so your expected loss conditional on a sale is 1. An unconditional prior mean of 5.5 misses the selection.

3. A small algorithm and its invariant

How can you return the largest value in a nonempty stream of n numbers without storing the whole stream?

Answer. Keep a running maximum. Initialise from the first value and replace it whenever the next value is larger. After each step it is the maximum of all values seen. Time is O(n), extra space O(1); an empty input needs an explicit API rule.

Topic practice behind this plan

Free practice tools

Primary sources and scope

Employer information was checked on the review date. Our study activities and original questions are editorial suggestions. Recruiting requirements, regional processes and vacancies can change; the employer’s current instructions take priority.