“I joined G-Research in 2019, having previously worked within financial services within the investment management and private wealth space.
“What I’ve enjoyed most about working at G-Research is the people and how willing they are to invest their time and effort in order to achieve the best possible outcomes; everyone here is collaborative and always willing to help. And that’s one piece of advice I’d give to anyone joining G-Research – invest time in getting to know the people as well as the business!”
"I enjoy how dynamic the work environment at G-Research is. It keeps you busy and continuously creates opportunities to develop yourself and your career, too."
"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."
"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."
"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."
"While some people might think working in finance may not be too exciting, at G-Research, it is, especially if you see it as a problem to solve. How do we solve this algorithm? How do we get faster? This is why I think people are really excited to work at G-Research."
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.
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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 As technology evolves, outdated code becomes inefficient and harder to maintain.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.
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