Quantitative Researcher, Mid-Frequency Trading (London)

eFinancialCareers; Greater London; On-site; GBP 100,000 - 160,000; Full time

Job summary

The firm is seeking a Quantitative Researcher to develop mid-frequency trading strategies across traditional and digital asset markets. You will work with trading and engineering teams to refine models, improve execution, and explore new alpha sources using a sophisticated research platform.

Responsibilities include backtesting, simulation, and scaling strategies across venues while leveraging Python and statistical methods to capture microstructure signals in digital assets.

Qualifications

• Experience developing systematic trading strategies with demonstrable performance.
• Strong academic background in Mathematics, Statistics, Computer Science, Engineering, or a related field.
• Proficiency in Python (C++ or other low-level languages is a plus)
• Solid understanding of statistical modelling, time series analysis, and market microstructure.
• Interest in applying quantitative strategies to digital asset markets.
• Strong collaborative and problem-solving mindset.

Responsibilities

• Develop and implement mid-frequency trading strategies (from seconds to multi-day holding periods)
• Design predictive models to capture inefficiencies in digital asset markets
• Analyse high-frequency and tick-level data to identify alpha signals and microstructure patterns
• Conduct robust backtesting, simulation, and optimisation of strategies
• Partner with engineering teams to improve execution and system performance
• Iterate on and scale strategies across multiple trading venues

Skills

Python proficiency

Statistical modelling

Time series analysis

Collaborative mindset

Education

Strong academic background in Mathematics, Statistics, Computer Science, Engineering

Tools

Exchange connectivity

Electronic trading systems

Job description

The firm is seeking a Quantitative Researcher to develop mid-frequency trading strategies across traditional and digital asset markets. You will work with trading and engineering teams to refine models, improve execution, and explore new alpha sources using a sophisticated research platform.

Responsibilities include backtesting, simulation, and scaling strategies across venues while leveraging Python and statistical methods to capture microstructure signals in digital assets.