Moving from Academia to Industry
At G-Research, we hire some of the brightest minds in the world to help us predict movements in financial markets.
Because of that hiring focus, a lot of our newest Quantitative Research and Engineering recruits join us not long after completing their PhD.
Moving from academia to industry can be daunting, which is why we place a big emphasis on making it as straightforward as possible, which is helped a lot by the academic environment of the business.
“One thing for people joining G-Research from academia is that it doesn’t feel like that much of a leap,” said Chloe, HR Business Partner for Research.
“We’ve got lots of other academics who say how the relaxed nature feels similar to the way their PhD did.”
As well as an academic, collegiate environment, a large part of smoothing that transition is ensuring the right support is in place for new starters as well.
Our new Researchers are paired with mentors on joining, as well as a quant buddy outside of their immediate team, which means they have a day-to-day mentor to help them, and someone from another team who can help with learning another piece of the puzzle. And it seems to work.
“I have to say the mentorship here is world-class, something I was looking for when starting a new role. This helped me bridge the divide between something that was more theoretical versus something that was more applied and more useful for everyday research,” Yang, Quantitative Researcher.
A cornerstone of studying for a PhD is individual research and while we pride ourselves on collaboration, our Researchers are given freedom and autonomy to do what they do best.
“It’s great just having the freedom to allocate my time, read some papers, read blog posts, and keep up with the field,” says Sebastian, Senior Quantitative Researcher.
“We make a lot of effort to stay connected to the research community to stay on top of recent developments. If there’s a big paper this will often be shared immediately, so I definitely still feel connected to the broader research community.”
That viewpoint is shared by another of our Quantitative Researchers, Clément:
“My role as a quant is incredibly autonomous. There are many other people supporting roles that are also autonomous to various degrees; the people closest to us have a lot of autonomy because we have broad research questions.
“I would compare that autonomy to a PhD, where you decide exactly what you want to do.”
Ongoing training and support
We work in a fast-paced environment within an ever evolving industry, where disciplines like Machine Learning (ML) are always moving forwards.
As such, the freedom to stay on top of the latest developments is also complemented by strong in-house learning options, summed up here by Chloe:
“We care a lot about learning and development. We’ve got the best talent who want to continue to learn, and we’re quite dynamic in that respect.
“We do many things, such as our ML boot camp, so new joiners can hit the ground running, but we’re also very good at nuanced training. If someone is struggling with something and we don’t have an out-of-the-box solution, we create something that works and then maintain it for future starters.”
That approach is something that new starters value, particularly if your background isn’t in ML, as Clément outlines:
“When you join, if you haven’t done any ML because you come from physics or maths, G-Research offers six weeks of non-stop courses, a fairly significant investment on a new joiner, but it gets you up to speed in every direction.”