Machine Learning

Machine Learning is an integral part of G-Research’s work. We use applied ML techniques to develop successful investment management strategies; it is one of the core drivers of our overall performance and success. ML has long been a key tool at G-Research and we count among our number a range of ICML and NeurIPS published researchers.

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Incentivised to explore the state-of-the-art

Our ML researchers have huge amounts of (clean) data and massive compute at their fingertips, with which they predict the future of financial markets. Because this is a very mature prediction problem, finding the 1% of difference, working at the very cutting edge of developments, is the place where success happens. We are, in effect, incentivised to explore the state-of-the-art. We do not work on cool problems for the sake of it, we work on them because we know one small discovery tilts the balance in our favour. While we start with the standard ML toolkit, to make a model work our researchers really need to understand what’s going on, not just throw an out-of-the-box solution at a dataset.

Quantitative feedback

Unlike pure problems, our researchers get near-instantaneous feedback on their work in the form of absolute numbers. There is no dotting i’s and crossing t’s (unless that improves your work): you build a model, back-test it extensively, then put it into production. It either works or it doesn’t – there is no debate or politics, just quantitative outcomes.

Collaborate on a common code base

A typical project lifecycle starts with the pencil and paper stage, and instead of selling your work, we run back tests. If it’s profitable, that’s enough. Our equivalent of sending off to peer review in academia is going to out-of-sample stage. Rather than publish we put research into production. As well as thousands of GPUs and tens of thousands of CPUs, our researchers share a common code base, tools, and techniques, collaborating on these to the benefit of everyone. We are also actively exploring next-generation forms of computing to maintain our edge.

Coronavirus Update

While the UK COVID-19 lockdown continues, G-Research is happy to confirm that we will continue to interview, hire, and onboard new staff remotely. Please do not hesitate to send in your application for a role.

Interview Process

What we look for You’ll have a record of academic achievement in mathematics, physics, machine learning, computer science or engineering. There’s no need for experience in finance.

Interview process

Quick and easy: we’ll ask for your CV and a few personal details like your education and contact information. Our Talent Acquisition team will review your application to see if you would be a good fit for the role. You should receive an update on the status of your application within one week of applying.

We’ll then ask you to take a 90 minute technical test to demonstrate excellence in mathematics, statistics, programming, and probabilities. This will be in person at our offices (if you’re in the UK), or at a remote testing location (if you’re based elsewhere, or depending on national lockdown requirements).

This stage follows the written assessment and is a technical interview building upon the areas tested in the Quantitative Research Assessment. If you’re based in the UK, this will immediately follow your written assessment at our offices. For those outside the UK (or depending on lockdown conditions), we’ll conduct this stage over Video Conference call.

This stage will cover more in-depth technical questions in mathematics, plus your choice of two subjects from programming, statistics, machine learning, and finance.

Following the successful completion of the Technical Interviews, we will assess your skills in relation to specific managers’ teams and their areas of research. Culture and team fit will also be assessed at these final stages.

Assessment Process Guidance

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Sample Questions

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