We are a proprietary quantitative trading firm specialising in high-frequency cryptocurrency markets. Trading exclusively with our own capital, we design and deploy systematic strategies that compete across global exchanges in a fast-moving, research-driven environment.

We're looking for an experienced Quantitative Researcher to take ownership of trading strategies from initial hypothesis through to live production and ongoing performance optimisation. This is a senior research role with genuine autonomy, direct impact on PnL, and the opportunity to shape the next generation of our trading strategies.

The Role

You'll own the complete research lifecycle, identifying market inefficiencies, developing predictive signals, validating ideas through rigorous analysis and working closely with our engineering team to deploy strategies into production.

Once live, you'll continuously analyse performance, refine trading logic and improve execution quality as market conditions evolve.

What You'll Do

Research & Signal Generation

1. Develop hypotheses around short-term market behaviour and market microstructure.
2. Research predictive signals using order book dynamics, trade flow, fill behaviour and short-term price evolution.
3. Analyse large datasets to identify repeatable sources of trading edge.
4. Design robust statistical tests to validate research ideas before deployment.

Strategy Development

1. Design, build and refine systematic HFT and market-making strategies.
2. Work closely with engineers to translate research into production systems.
3. Contribute to simulation, backtesting and evaluation frameworks.
4. Iterate rapidly based on research findings and live trading feedback.

Live Strategy Ownership

1. Monitor live strategy performance and PnL attribution.
2. Perform detailed post-trade analysis to understand strengths and weaknesses.
3. Improve execution quality while balancing latency, inventory risk and spread capture.
4. Continuously refine trading logic as market conditions and edge evolve.

What We're Looking For

We're interested in researchers who have made meaningful contributions to successful live HFT trading strategies and enjoy taking ownership of ideas from research through to production. In most cases, successful candidates will have at least two years of hands-on experience researching or developing live HFT or market-making strategies within a proprietary trading firm or quantitative trading team.

You should have:

1. At least two years' experience researching and developing live HFT or market-making strategies within a proprietary trading firm or quantitative trading team.
2. A strong understanding of market microstructure, order book dynamics and short-term price formation.
3. Experience contributing to or owning strategies that have traded successfully in live HFT environments.
4. Strong research methodology with disciplined hypothesis testing, statistical analysis, backtesting and validation.
5. Excellent Python skills and experience analysing large datasets.
6. Strong analytical thinking with the ability to communicate research clearly across technical and non-technical stakeholders.
7. A collaborative mindset and a genuine curiosity for markets and systematic trading.

Particularly Relevant Experience

Experience in several of the following areas would be highly valued:

1. High-frequency trading or market making in crypto or traditional markets.
2. Proprietary systematic trading.
3. Short-term alpha generation.
4. Order book mechanics and market microstructure research.
5. Execution optimisation, slippage analysis and adverse selection.
6. Cross-exchange arbitrage.
7. Performance attribution and post-trade analysis.
8. Strategy evaluation, backtesting and risk management.

Nice to Have

1. Direct experience trading crypto markets.
2. Experience with centralised and decentralised exchanges.
3. Cross-exchange arbitrage or DEX strategies.
4. Understanding of latency-sensitive systems.
5. Experience balancing inventory risk, execution quality and spread capture.
6. SQL and advanced .NET experience.

Education

We typically look for candidates with an academic background in Mathematics, Physics, Computer Science, Engineering, Statistics or another closely related quantitative discipline. Applicants from other backgrounds will also be considered where they can demonstrate an exceptional track record of quantitative research and live HFT strategy development.