Quantitative Research

Quantitative Research

We use the latest scientific techniques and advanced data analysis methods to discover the undiscovered. Our researchers are free to explore ideas, finding patterns in large, noisy and real-world data sets to predict the movements in global financial markets.

Quantitative Research

Our technology and resources are combined to build a single, powerful platform for researching algorithmic trading strategies. 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

Our mission is to develop models to forecast financial time series. This is a challenging and highly competitive space so rather than deploy standard methods off the shelf you will likely need to extend classical methods or develop entirely new techniques. Our problems are well-defined and success is highly measurable and has direct impact on the business. 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 Mathematicians

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.

Meet Tom | Quantitative Research Manager

Tom Joined G-Research in 2009 after working for an investment bank. Tom studied Mathematics at the University of Cambridge and Berkeley University before doing several postdocs at various universities in Europe and the US.

How did I become a quant?

Personally, I come from a pure mathematics background. After completing my PhD in the United States, I spent several years doing various postdocs, including two and a half years in London.

At this point I decided to leave academia as I wanted to live in London, since my wife was already working here, and to be frank, I wanted to be able to afford to buy a house!

Also having seen the amount of administrative work and grant applications that were involved in academic careers, and the general level of morale in departments I knew well, I no longer felt excited about academia. I wanted to work in a more collaborative environment and work on more applied projects that would be of interest to more than a handful of other pure mathematicians… Read More

Learn more about our 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

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, however, no prior preparation is required. You can get an idea of what to expect by reviewing our suggested reading list and attempting our sample test questions

Suggested Reading

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Sample questions

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