Quantitative Trader

Confidential; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Confidential in London is seeking a Quantitative Trader to join a leading proprietary trading firm. You will develop, execute, and enhance systematic trading strategies across global markets, collaborating with researchers, traders, and tech teams.

The role emphasizes backtesting, data-driven analysis, and automated execution, with ownership from research through implementation and ongoing improvement of strategies.

Qualifications

• 3–8 years of experience in quantitative or algorithmic trading.
• Experience at a proprietary trading/HFT environment.
• Bachelor's degree in Computer Science and Engineering.
• Strong programming and quantitative skills.

Responsibilities

• Identify, develop, maintain, and enhance systematic trading strategies.
• Build, backtest, and improve scalable quantitative trading models.
• Analyse trading performance and financial time-series data using statistical and data-driven methods.
• Develop systematic approaches to automate trading strategies and improve execution.
• Construct, test, maintain and continuously improve financial models.
• Work closely with technology and engineering teams to maintain and enhance electronic trading systems.
• Monitor trading performance and risk, with a strong understanding of risk management procedures.
• Investigate market behavior, identify opportunities and translate quantitative insights into actionable trading strategies.
• Take ownership of strategies through the research, backtesting, implementation and improvement lifecycle.

Skills

C++

Python

Quantitative analysis

Statistics

Time-series analysis

Backtesting

Data structures

Risk management

Independent work

Education

BSc in Computer Science & Engineering

Job description

We are looking for a Quantitative Trader to join a leading proprietary trading firm and work on the development, execution, and enhancement of systematic trading strategies across global financial markets.

The role involves working closely with quantitative researchers, traders, and technology teams to identify trading opportunities, build and backtest scalable strategies, and continuously improve trading performance.

The ideal candidate will have prior experience within a proprietary trading / algorithmic trading environment, combined with strong quantitative and programming skills.

Key Responsibilities:

• Identify, develop, maintain, and enhance systematic trading strategies
• Build, backtest, and improve scalable quantitative trading models
• Analyse trading performance and financial time-series data using statistical and data-driven methods
• Develop systematic approaches to automate trading strategies and improve execution
• Construct, test, maintain and continuously improve financial models
• Work closely with technology and engineering teams to maintain and enhance electronic trading systems
• Monitor trading performance and risk, with a strong understanding of risk management procedures
• Investigate market behavior, identify opportunities and translate quantitative insights into actionable trading strategies
• Take ownership of strategies through the research, backtesting, implementation and improvement lifecycle.

Requirements:

• 3-8 years of relevant experience in quantitative trading, algorithmic trading, or a closely related role
• Prior experience at a proprietary trading firm, HFT firm, market maker, or quantitative trading environment
• Bachelor's degree in Computer Science and Engineering, preferably from a top-tier institute
• Strong quantitative, analytical, and problem-solving abilities
• Strong understanding of probability, statistics, and financial time-series analysis
• Strong programming skills in C++ and/or Python
• Strong understanding of data structures and algorithms
• Experience building, backtesting, and analysing systematic trading strategies
• Good understanding of trading systems, execution, and risk management
• Ability to work independently and take ownership of trading strategies and outcomes