Quantitative Research & Machine Learning


We apply scientific techniques to find patterns in large, noisy and real-world data sets, using the latest statistical and “big data” analysis methodologies to predict global financial markets.

Quantitative Research

Our technology, research and resources are combined to build a single, powerful platform for developing your ideas. We use rigorous scientific methodology, robust statistical analysis and pattern recognition to analyse an extensive and varied financial data ecosystem, extracting deep insights from truly massive datasets. Our platform provides the ability to test your mathematical models in action and get instant results using real world data.

Machine Learning

We employ cutting edge machine learning methods drawn from diverse areas such as neural networks and deep learning; non-convex optimisation; Bayesian non-parametrics and approximate inference. We have the freedom to extend classical methods as well as develop entirely new ideas.

Inspirational Mathematics

At G-Research we promote an academic and intellectual culture. Most of our Researchers have joined from PhDs or Postdocs from top-tier global institutions. There are multiple IMO medallists, Fulbright Scholars and even a Senior Wrangler.

Team Profiles



“G-Research’s interview process convinced me that they only hire top mathematical talent, so I knew they were carrying out difficult and rigorous research.”

I joined G-Research after completing a PhD in astrophysical fluid dynamics at Cambridge in 2015. Finance was an attractive option for me because of the opportunity to carry out work of a more statistical nature than my previous research. Day to day, it’s great to be working in a team with other highly skilled Researchers. It’s even more satisfying when you get a result that really makes a difference.



“You hit the ground running here, with the freedom to work on any idea you have”.

I studied Mathematics at Princeton University before obtaining a PhD in mathematics from the University of California, Berkeley. My main research area was probability theory, where I focused on eigenvalue distributions of certain discrete matrix groups. I chose G-Research because it provides researchers the best environment for developing new ideas and making significant contributions very quickly.



 “G-Research has been a great move from academia.”

Prior to joining G-Research, I was a mathematician at Oxford, completing a PhD in 2011 and then working as a PostDoc. Finance appealed to me as a field where maths research could have a real and immediate impact. G-Research is genuinely a research-lead company that gives us cutting-edge tools and world class data to study some of the most interesting problems in the industry.



“I moved to G-Research seeking a new challenge that would let me make full use of my machine learning experience. I have not been disappointed”.

After reading computer science at Cambridge, I completed a PhD on the topic of Gaussian processes. Since I joined G-Research, the discipline of machine learning has grown to become a foundational part of the company’s quantitative approach to investment. There are limitless opportunities here to apply innovative techniques to some uniquely difficult data sets.

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

You’ll take a 90 minute, handwritten technical test to demonstrate excellence in maths, stats, programming and probabilities. This is followed by interviews. The assessment process is highly challenging, so we suggest you spend some time preparing for the test by reviewing our suggested reading list and attempting our sample test questions. Links below:

Suggested reading

Sample questions

Want to work with us?

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Want to be part of a leading quantitative research and technology company? Bring your skills and experience to G-Research by applying for one of our many roles.

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