The Role

We're looking for exceptional quantitative researchers and traders to help develop, enhance and deploy systematic trading strategies across global digital asset markets. This is a hands-on research role where your work will have a direct impact on live trading in a fast-moving, always-on environment.

Working closely with traders and engineers, you'll investigate market behaviour, generate new ideas, improve execution and contribute throughout the full research lifecycle—from hypothesis generation and backtesting through to production deployment and ongoing optimisation.

Responsibilities

1. Research market microstructure and identify exploitable trading opportunities across both centralised and decentralised digital asset markets.
2. Design, develop and evaluate systematic trading strategies using large-scale market data.
3. Enhance execution algorithms, reduce transaction costs and improve trading performance.
4. Build and refine research tools, simulation frameworks and analytical infrastructure alongside engineering teams.
5. Monitor live strategies, analyse performance and continuously iterate on models based on real-world results.
6. Contribute to the development of scalable research processes and quantitative infrastructure.

Requirements

We're interested in candidates who demonstrate:

1. Strong programming ability, ideally in Python or another quantitative programming language.
2. A degree in Computer Science, Mathematics, Physics, Statistics, Engineering or another highly quantitative discipline.
3. Experience conducting quantitative research, systematic trading, machine learning or solving complex data-driven problems.
4. Excellent analytical skills with the ability to work from data to actionable insights.
5. A naturally curious mindset and a genuine interest in financial markets and algorithmic trading.

Desirable Experience

Any of the following would be advantageous:

1. Previous experience researching or developing algorithmic trading strategies.
2. Knowledge of electronic market microstructure, execution algorithms or market making.
3. Familiarity with cryptocurrency markets, digital assets or broader financial markets.
4. Experience working with large datasets, distributed systems or low-latency trading environments.