Quantitative Trader
Own the complete lifecycle of systematic trading strategies: from an initial market observation to reliable live execution. Join a small, collaborative desk where traders and engineers share responsibility for performance.
Your work.
- Research market microstructure and identify repeatable sources of trading opportunity.
- Design, test and monitor systematic strategies across centralised and decentralised venues.
- Work with engineering to improve execution performance, observability and controls.
- Communicate portfolio behaviour and trading risks clearly to the wider team.
Your perspective.
- Strong quantitative reasoning and a record of rigorous problem solving.
- Comfort working with Python, large datasets and statistical modelling.
- Practical understanding of electronic markets and execution mechanics.
- Clear communication, intellectual curiosity and sound risk judgement.
The team you’ll join.
You will work alongside colleagues who value clear thinking, useful feedback and shared ownership. Our trading, research, engineering and business teams work closely across London, Singapore and Dubai. We keep communication direct and make room for different approaches to solving a problem.
How we hire.
We start with a conversation about your experience, interests and the work you want to do. Relevant technical or practical discussions follow, giving you a clear picture of the role and the people you would work with. The process is tailored to the position, and our team will explain each step in advance.
We welcome applications from people with different backgrounds and paths into the industry. Tell us about the problems you have solved and how you think. If you need an adjustment to participate in our process, include that in your enquiry.
Good work starts here.
Think precisely.
We examine assumptions, ask direct questions and use evidence to make decisions. Clear thinking creates the foundation for useful action.
Build together.
Markets connect disciplines. So do we. Traders, engineers and researchers work closely, share context and take responsibility for collective outcomes.
Keep improving.
We learn from real results, revisit our models and invest in better tools. Small, consistent improvements compound into a meaningful advantage.