Quantitative Research
We are a team of engineers, mathematicians and scientists working to predict the future.
Solving financial markets presents one of the most fascinating, complex, high-stakes quantitative problems in existence today.
Markets are always evolving. They change every nanosecond, everywhere around the globe, generating trillions of data points.
To navigate them successfully, we design trading algorithms that rely on complex ML models to anticipate moves. In a hyper-competitive space like trading, we are constantly innovating at speed to win.
our expertise
What we work on
Machine learning
Our researchers work with petabytes of multi-dimensional, non-stationary and noisy market data to build price forecasting models that drive our trading algorithms. These rich datasets create opportunities for solving research problems at scale. Some problems require novel architectures. Others demand scaling known techniques 10–100x beyond their original design limits.
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Mathematical modelling
We trade a constantly evolving set of complex, interrelated financial instruments. Our researchers design pricing engines to capture these relationships, ensuring consistency in our market view. They directly monitor how models affect strategies, ensuring changes propagate across products and risk is weighed against reward. Balancing the requirements for accuracy, performance and reliability at scale makes the modelling problem both challenging and rewarding.
A culture built for breakthroughs
Fast research and development cycles
We don’t build for clients, we build for ourselves. Ideas are tested rapidly and rolled into production immediately.
Investment in breakthroughs
We dedicate a large share of our resources towards longer term R&D. Working at the bleeding edges of computer science and machine learning.
Shared success
Discoveries don’t stay siloed. Research findings are scaled across all dimensions: markets, product types, and time horizons. This ensures that the work of one team drives progress for the entire organisation.
Open collaboration
We embrace an open, collaborative culture because it enables multidisciplinary problem solving and allows us to build on the insights of many. For that reason, intellectual property and code repositories are kept widely accessible across research teams.
Ownership
We replace tasks with end-to-end ownership of complex problems and research areas. We set the vision, decide the most important problems to tackle, and guide each solution from concept to execution.
Picture yourself here
- A Bachelor’s, Master’s or PhD in mathematics, physics, computer science, engineering or a related STEM field
- Experience in machine learning, statistical modelling and large dataset management (e.g. time-series analysis)
- Solid mathematical foundations and programming proficiency, preferably in Python, C or C++
- Outstanding communication skills, a commitment to continual learning and a competitive mindset
open roles
Opportunity awaits
Quant research
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