Quant Trading Interview Guide

Quant interview processes vary across firms. Some focus heavily on standardized technical screens, brainteasers, or credentials. Ours is built for a different purpose.

At QSG, we are less interested in whether someone can perform well in a narrow interview format and more interested in whether they can do the actual work: think clearly, structure research, handle ambiguity, communicate honestly, and develop over time.

We are trying to understand how you think, what you build, how you respond to feedback, and whether you are serious about markets.

Many traditional quant interview processes are designed for firms hiring at scale. They often rely on standardized filters such as school, GPA, technical screens, coding problems, and probability questions.

That approach can work for large institutions, but it does not always tell you much about whether someone can actually build, iterate, and grow in a live trading environment.

Our process is designed around different questions:

• Do you show initiative? 
• Can you take an open-ended problem and structure it well? 
• Do you understand your own work deeply enough to explain and defend it? 
• Can you recognize mistakes and improve them? 
• Are you genuinely interested in markets and in the day-to-day work of becoming a trader? 

We are not trying to filter for polished interview performance alone. We are trying to identify people who may develop into strong traders and researchers over time.

We care about a small number of things, but we care about them a lot.

We want to see that you can take a messy problem and break it into reasonable pieces. That includes defining the objective, making sensible assumptions, understanding tradeoffs, and communicating limitations.

A strong candidate project is not just an interesting idea. It is work that can be reviewed end to end.

That means:

• code is organized and readable 
• assumptions are clear 
• data handling is sensible 
• validation choices are explainable 
• results can be reproduced 

We expect candidates to submit complete code and whatever instructions are needed to run it and reproduce results end to end. If the work cannot be reviewed or reproduced, it is much harder for us to evaluate.

We are much less interested in whether a candidate appears to have found some alpha in a synthetic dataset than in whether they handled the exercise properly.

We pay attention to things like:

• leakage and lookahead risk 
• weak train/test design 
• fragile assumptions 
• poor handling of messy data 
• lack of reproducibility 
• confusing or inconsistent bookkeeping 

A project with modest results but strong research discipline is often more impressive than a project with flashy results and weak foundations.

Candidates often receive follow-up questions on their project. This is not meant to be adversarial. We use follow-up to understand how deeply you understand your own work and how you respond when something is challenged.

If a decision was intentional, you should be able to explain and defend it clearly.

If you made a mistake, it is usually much better to identify it directly and fix it than to dig in and defend something that does not hold up. We respect clear thinking and honesty much more than unnecessary defensiveness.

We are looking for candidates who are genuinely interested in markets, trading, and strategy development. That does not require a long professional track record. It does require real curiosity.

We like to see evidence that someone has tried to build, test, analyze, or explore something on their own, even if the work is early or imperfect.

The project is a central part of our process because it gives us something much more useful than a resume. It shows how you actually work.

A strong project usually demonstrates:

• clean code 
• sensible structure 
• thoughtful treatment of data 
• awareness of pitfalls 
• reasonable experiments 
• clear explanation of results and limitations 

What it usually does not require is finding a magical signal in the data.

Candidates sometimes overfocus on trying to impress us with performance. That is often the wrong goal. We usually learn more from the way you frame the problem, structure the work, and explain your decisions than from the headline result itself.

After reviewing a project, we often follow up by email with questions about the work.

This may include questions about:

• assumptions 
• implementation choices 
• validation 
• edge cases 
• mistakes or inconsistencies we noticed 
• what you would improve with more time 

This part matters. In many cases, it is the bridge between the project and the technical interview.

A strong response does not require perfection. It requires clarity, ownership, and the ability to think through your own work carefully.

Our interviews are not just about whether you can answer technical questions in isolation. They are meant to help both sides understand whether this work is a real fit.

Interviews may explore:

• your candidate project 
• prior research or coding work 
• probability and decision-making 
• strategy and market reasoning 
• how you communicate under uncertainty 
• how you respond to feedback 
• whether you actually enjoy this type of work 

We may also use short exercises or discussions involving probability, games of chance, strategy, or market scenarios to better understand how you think.

The best preparation is usually straightforward:

• know your own work well 
• be ready to walk through your code and decisions end to end 
• be honest about strengths and weaknesses 
• be ready to explain tradeoffs and limitations 
• be genuinely thoughtful about why you want to do this work 

Trying to optimize for what you think we want to hear is usually less effective than understanding your own work deeply and speaking plainly about it.

Our process is not designed to reward polished interviewing alone. It is designed to help us identify candidates who may develop well once they are given real exposure to research, markets, execution, and feedback.

We are looking for people who can build, think, learn, and improve.