Hudson River Trading interview questions

How Hudson River Trading's process runs, as candidates and the firm describe it, and the questions candidates report, each with a worked solution. Free, no sign-up.

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How Hudson River Trading interviews

Sources: Glassdoor: HRT conditional dice question · Glassdoor: HRT coding test

Questions (3)

Try each one before opening the solution. Interviewers change the numbers, so learn the method.

Quantitative Trader · Interview · Conditional probability · reported by a candidate

You roll a fair die until the first 6. Given that a 3 appeared before that first 6, what is the expected number of rolls (including the 6)?

  1. Expected number of rolls, given a 3 came first:
    Answer and worked solution

    Answer: 9

    Key idea. E[N | A] = E[N; A]/P(A), where N is the rolls until the first 6 and A is "a 3 came first".

    1. P(A) = ½ by symmetry between 3 and 6.
    2. Given N = n, the earlier n − 1 rolls are uniform on 1–5, so P(not A | N = n) = (4/5)n−1.
    3. E[N; not A] = Σ n (5/6)n−1(1/6)(4/5)n−1 = (1/6) Σ n (2/3)n−1 = (1/6) × 9 = 1.5.
    4. E[N; A] = 6 − 1.5 = 4.5, so E[N | A] = 4.5/0.5 = 9.

    Check. Longer than the unconditional 6: games long enough to include a 3 tend to be longer.

    Trap. Answering 6 + something small, or arguing the 3 "doesn't matter".

Interviewers may push further: Compare: expected rolls until a 6, given every roll before it was even. (1.5, not 3.)

Source: Glassdoor · Try it in the app →

Trader · Interview · Behavioural · reported by a candidate

Behavioural questions candidates report:

Answer one out loud, as you would in the interview.

Model answer

Use a short story: the situation in one sentence, what you did (not the team), the result with a number if you have one, and what you'd do differently. Aim for about two minutes.

Expect to be pushed on details: why that method, what you tried that failed, how you'd know if the result was wrong. Pick a project you can defend line by line.

Source: Glassdoor: HRT quantitative trader (Aug 2026) · Try it in the app →

Algo Developer (campus) · Interview · Expected value · reported by a candidate

A point is placed uniformly at random in a 1 × 1 square.

Reported as "expected distance to an edge, with a randomly placed object". We use the usual version: a point uniform in a unit square, distance to the nearest edge.

  1. What is the expected distance to the nearest edge?
    Answer and worked solution

    Answer: 1/6 ≈ 0.16667

    Key idea. Use the tail formula E[D] = ∫ P(D > t) dt.

    1. D > t means the point is at least t from every edge: inside a central square of side 1 − 2t.
    2. P(D > t) = (1 − 2t)² for 0 ≤ t ≤ ½.
    3. E[D] = ∫₀½ (1 − 2t)² dt = 1/6.
    4. 1/6.

    Check. The farthest any point can be from an edge is ½ (the centre), and most points are near an edge, so a small answer makes sense.

    Trap. Answering ¼ (the mean distance to one particular edge, not the nearest).

Source: Wall Street Oasis: Hudson River Trading interview reports · Try it in the app →

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