Quantitative Researcher
Posted Sep 2, 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 research project where you built a predictive model from raw data to something that actually got used. What was the hardest part?
What they are checking: whether the candidate has end-to-end research ownership, not just model fitting
Tell me about a time your model or signal looked great in backtest but failed or underperformed once it was live. What did you do?
What they are checking: awareness of overfitting and how they respond to being wrong in a fast market
Tell me about the most rigorous piece of research you've done, academic or professional. What made you confident the result was real and not noise?
What they are checking: depth and honesty of research process, since the posting explicitly wants a proven research background
How do you keep yourself updated on new machine learning techniques, and can you give an example of one you tried applying to a real problem, even if it didn't work out?
What they are checking: genuine curiosity and hands-on experimentation versus buzzword familiarity
What would you do
Tell me about a time you disagreed with a trader or a colleague about whether a model or strategy was ready to go live. How did you resolve it?
What they are checking: ability to collaborate and communicate technical judgment across a multidisciplinary team
If you inherited a research pipeline that was slow, undocumented, and hard for others to reproduce, what would you change first and why?
What they are checking: instinct for scalable, reproducible research infrastructure rather than one-off scripts
Say the market suddenly moves in a way your model didn't anticipate and your desk needs an answer in minutes, not days. What do you do?
What they are checking: ability to think and communicate quickly under real market pressure
If two of your colleagues on the team have very different intuitions about a market and you're building a shared model, how do you reconcile that in your research?
What they are checking: comfort working in a multidisciplinary team where traders and researchers may see things differently
About the work
Suppose you have a dataset of options quotes and trades and you want to find a predictive feature for short-term price movement. How would you go about building and validating that feature?
What they are checking: practical feature engineering and validation discipline, not just naming algorithms
How do you decide between a simpler statistical model and a more complex machine learning model for a given trading signal?
What they are checking: judgment about tradeoffs between interpretability, speed, and predictive power in a trading context
Explain overfitting to someone non-technical, then tell me how you'd detect it in a research pipeline that has hundreds of features and limited data history.
What they are checking: depth of statistical understanding and ability to communicate it simply
You are given a coin that you're told is biased. How would you design an experiment or statistical test to estimate the bias and how confident would you be in your answer?
What they are checking: core probability and statistics fluency under time pressure
Walk me through how you would price a simple option and what assumptions in that model worry you most in a live, fast-moving market.
What they are checking: grasp of options fundamentals and where theory breaks down in practice
Describe a piece of code you wrote in Python or C++ that had to run efficiently at scale. What made it fast, and what would you optimize next?
What they are checking: actual programming competence versus academic-only coding experience
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Practice this out loudThe full job description
As published by Akuna Capital.