Our researchers use the latest scientific techniques and advanced statistical analysis methods to predict movement in global financial markets.
Our technology and resources are combined to build a single, powerful algorithmic trading platform for client use.
We use rigorous scientific methodology to analyse an extensive data ecosystem, extracting deep insights from truly massive datasets. Our platform allows our researchers to test, model and get instant results. We then design and develop state-of-the-art optimisation techniques to maximise the value of every idea.
Our researchers have a challenge: disproving the efficient market hypothesis every day. This requires them to harness massive compute power and to use state-of-the-art ML techniques – published in recent conferences or developed entirely in-house – as textbook methods won’t beat the competition.
We don’t just hire some of the best ML practitioners in the world, we also develop the next generation of talent too, through G-Research Machine Learning College.
Our culture is collaborative and intellectual. Most of our researchers have joined from PhDs or Postdocs from top global institutions, and have had their work published at the most prestigious conferences in the world.
"I continue to be blown away by the quality of the people I get to work with here: G-Research thrives because of its team culture and we’ve hired carefully to make sure everyone is both exceptionally smart and great to work with."
"G-Research makes a lot of effort to have a very open culture and gives a lot of freedom to its individual researchers to pursue directions that they think are valuable, with each researcher very much driving their own research. I didn’t feel like I was losing a lot of freedom (compared to academia)."
"What I like the most about my job is it’s super open. I’m able to work with a lot of folks from other teams, too, such as working closely with Engineers and other Quantitative Researchers."
"My role focuses on finding signals in real-world data and in many ways, it feels like a continuation of my PhD; I’m looking at unexplored problems and I choose which ones to focus on."
"There was a lot I didn't know about the G-Research, so I gained insights from those who interviewed me. They all came across as intelligent, curious, and interested in exploring problems from different angles. I figured if people like this enjoy their jobs then I most certainly will."
"The two biggest things that I like about working at G-Research are the smart and incredibly friendly colleagues, as well as being able to strike a really good work-life balance (in contrast to a lot of the finance industry)."
Thrive in a collaborative, flexible environment where smart people learn and grow together.
Our quantitative researchers and machine learning practitioners have a record of academic achievement in mathematics, physics, ML, computer science or engineering. There’s no need for experience in finance.
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.
Hear from our Head of Forecasting Engineering on why the term "tech debt" has outlived its usefulness. In this blog, he explores why we should move away from generic labels and instead ask more precise, value-driven questions that lead to meaningful improvements in engineering and business outcomes.
Read article
Each month, we provide up to £2,000 in grant money to early career researchers in quantitative disciplines. Hear from our March grant winners.
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Hear from Jay, an Open Source Software Engineer, on tackling technical debt in OpenStack. As technology evolves, outdated code becomes inefficient and harder to maintain. Jay highlights the importance of refactoring legacy systems to keep open-source projects sustainable and future-proof.
Read article
We collaborate with researchers, design real-time platforms and process massive datasets at speed and scale.
We partner with and invest in the open source community.
We identify, test and on-board the latest tech, enabling our researchers and engineers to keep innovating.
