Junior Quantitative Researcher
Posted Sep 18, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Walk me through a time you built a statistical or machine learning model to solve a messy, real-world problem. What made the data or the problem hard?
What they are checking: whether the candidate has genuinely applied modeling skills beyond coursework, and can explain tradeoffs they made
Tell me about a time you had to make a decision quickly with incomplete information. What did you do?
What they are checking: whether the person can act decisively under pressure, which matters for a market-making desk
Tell me about a research project where your first approach didn't work. What did you change and why?
What they are checking: resilience and the ability to iterate on a failed hypothesis rather than abandon the problem
Describe a situation where you disagreed with a teammate about which approach to take on a project. How did you resolve it?
What they are checking: collaboration style, since the team explicitly blends mathematicians, statisticians, and technologists
What would you do
Suppose you're building a portfolio optimization algorithm and the market suddenly becomes much more volatile. How would you adjust your approach?
What they are checking: whether the candidate thinks about robustness and risk, not just optimizing for a static environment
How would you explain a machine learning model's prediction to a trader who doesn't have a technical background but needs to trust it before using it live?
What they are checking: communication skills and whether the candidate respects the gap between research and real trading decisions
Imagine you built a model that performs well historically but starts losing money live. Walk me through how you'd investigate what went wrong.
What they are checking: diagnostic thinking about model decay, regime change, and the difference between backtest and live performance
About the work
Say you have a coin that you believe is biased. How would you design an experiment to figure out the bias, and how many flips would you need to be confident in your answer?
What they are checking: probability and statistical reasoning under uncertainty, done out loud without tools
You're given a dataset of options prices and you notice a feature that seems highly predictive in-sample but you're worried about overfitting. How do you check whether it's real?
What they are checking: understanding of overfitting, validation techniques, and healthy skepticism toward promising results
Explain how you would price a European call option and what happens to that price as volatility increases.
What they are checking: basic derivatives intuition, since financial background isn't required but market curiosity is expected
If I flip a fair coin 100 times, what's the probability I get exactly 50 heads, and how would you approximate that without a calculator?
What they are checking: comfort with combinatorics and approximation methods like normal approximation to binomial, done mentally
Write or describe code that would compute a rolling correlation between two time series efficiently. What would slow it down at scale?
What they are checking: practical Python skill and awareness of performance considerations, since the role lists intermediate Python
You have a jar with 100 marbles, some red and some blue, and you draw 10 without replacement, getting 3 red. Estimate the number of red marbles in the jar and explain your reasoning.
What they are checking: quick, accurate quantitative reasoning under time pressure, mirroring the reactive math the job description calls out
Practice this interview out loud.
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Practice this out loudThe full job description
As published by Akuna Capital.