Quantitative Developer - Python
Posted Sep 30, 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
Tell me about a system you built that shortened the loop from idea to live trading. What changed for researchers?
What they are checking: Whether they measure their work by faster iteration and impact, which is the heart of this role.
Tell me about a time a pipeline you owned delivered bad data. How did you find out, and what did you change?
What they are checking: Ownership of data quality and whether they add checks so the same failure can't recur.
What would you do
A signal looks strong in research but loses money live. Walk me through how you debug that end to end.
What they are checking: Whether they can trace a gap across data, features, simulation and execution instead of blaming one piece.
A strategy optimizer suggests parameters that look great in backtest but trade a lot more. How do you decide whether to use them?
What they are checking: Whether they balance expected performance with costs, capacity and real-world limits.
A researcher wants a quick hack in production to test an idea, and you think it adds risk. How do you handle it?
What they are checking: Whether they can support fast iteration while protecting the reliability of live trading.
About the work
How would you build a backtester that models latency and queue position realistically, not just fills at the mid price?
What they are checking: Whether they know where simple backtests lie and how microstructure changes real results.
How do you make sure a feature computed in research matches exactly what the live system computes?
What they are checking: Whether they understand train and serve skew and how to build one pipeline for both paths.
When would you choose polars over pandas, and what have you run into using either at scale?
What they are checking: Real production experience with Python data tools and their performance limits.
How do you test a time series feature for lookahead bias?
What they are checking: Whether they guard against using future information, the most common way backtests fool people.
How would you define features that behave consistently across instruments that trade very differently, and across market regimes?
What they are checking: Their thinking on normalization and stability so features don't break when conditions change.
How do you apply machine learning to a systematic strategy without overfitting on noisy financial data?
What they are checking: Grounding in ML for trading, including validation, regularization and the low signal to noise ratio.
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Practice this out loudThe full job description
As published by IMC Trading.