A quant interview is your chance to show how you think, not just what you know. It’s where analytical skill meets creativity, and where the best minds stand out for how they approach the unknown.
At G-Research, we look for people who thrive on challenges and think differently – those who see problems as opportunities to innovate. This guide will help you prepare with purpose, understand what to expect and approach every question with confidence.
Your preparation starts here, and your quant career starts next.
Our hiring process isn’t just a test of what you know, we want to know how you think. We don’t just focus theory and code, we look for people who take ownership, ask questions and push ideas further. It’s a process that will challenge you, inspire you and give you the space to show what makes you different.
Explore each stage to see what we look for – and how to bring your best to each step.
Quick and easy: We will ask for your CV/resume and a few personal details like your education and contact information. Our Talent Acquisition team will review your application to see if you are a good fit and you will receive an update on the status of your application within one week of applying.
You will be asked to complete one of two quant quizzes; either a general quantitative aptitude assessment, or an ML specific one, depending on your background.
Typically, you will sit four interviews, one of which will focus on in-depth technical questions in mathematics. Each interview will last one hour.
If your profile is better suited to ML, you’ll complete two one-hour interviews, which will focus on your ML knowledge, but do expect to answer questions on mathematics, programming and stats that are relevant to the ML space as well!
Following the successful completion of the technical interviews, you will meet some of our leaders.
Our quiz and puzzle questions reflect the kind of challenges our researchers tackle every day: complex, fascinating problems that sit at the intersection of machine learning, maths, data and innovation. They’ll give you a glimpse into how we think at G-Research.
We don’t expect you to know everything. What matters most is being a naturally curious, creative thinker who has a genuine passion for solving problem and learning fast. If you’re strong in most areas but still mastering a few, that’s exactly the kind of mind we want to meet.
Great performance starts with great preparation. Mostly, we’re keen to understand how you think and approach problems, but having the theory nailed down will help you perform at your best.
Start with 150 Most Frequently Asked Questions on Quant Interviews – a classic for building range and confidence, focusing on numerical methods and problem-solving at the level of our triage interview. For our machine learning quiz, add Cracking the Coding Interview and The Elements of Statistical Learning to your list, alongside trusted online prep courses.
ProjectEuler.net is a nice place to start for coding practice, it provides bite-size maths problems that often require a computational solution. Codility, Kaggle and TopCoder have similar projects. If you want to know what other quants are reading, this community wiki answer provides lots of options, ranging from introductory to expert
One of the biggest mistakes candidates make is only focusing on theory. We want to see how you apply ideas to real problems and how you reason under pressure. Don’t overlook the soft skills. Quants rarely work alone, they work side-by-side with engineers, data scientists and other researchers, so being able to communicate and collaborate effectively is just as valuable as getting the theory right.
And finally, practice matters. The best candidates revise, simulate mock interviews and learn to think out loud. Go in ready to explore, not just to answer. Remember: it’s not about having all the answers, it’s about showing the mindset of someone who will find them.
Most of our researchers use Python and C++, but we value strong logical thinking over specific syntax. If you can write clean, efficient code and approach problems methodically, you’ll fit right in.
We take a holistic approach, combining technical assessments with opportunities to show your curiosity, collaboration and communication skills. It’s as much about potential and mindset as it is about raw knowledge.
The process typically involves several stages, from an initial online assessment to technical interviews and problem-solving discussions, usually completed within a few weeks.
In some instances, yes. We include these to see how you reason under pressure, not to catch you out. Talk through your thought process clearly, we’re more interested in your approach than the final answer.
Don’t panic. Take a moment, explain your thinking so far and outline possible next steps. Showing composure and logical reasoning is often more impressive than landing the perfect answer.
