Hybrid Quant Researcher, Systematic Equities & Alpha Signals

Radley James; Greater London; Hybrid; GBP 100,000 - 180,000; Full time

Benefits offered by this job

Hybrid working model

Job summary

Radley James in London is seeking a Quantitative Researcher for a mid-frequency systematic equities pod in a leading global hedge fund. You will perform end-to-end alpha research, including signal development, data analysis, backtesting and evaluation of predictive signals, collaborating with the PM and quants to translate research into robust strategies.

The role requires strong programming skills (Python or similar) and a quantitative degree, with a hybrid working model and competitive

Qualifications

• Experience as a Quantitative Researcher within a buy-side investment environment (hedge fund or systematic asset manager).
• Strong quantitative research methodologies including alpha generation, signal development, and backtesting.
• Desirable: experience in mid-frequency equities.
• Proficient in Python or other quantitative research languages.

Responsibilities

• Identify and develop new alpha sources across equity markets.
• End-to-end alpha research: signal development, data analysis, backtesting, evaluation of predictive signals.
• Collaborate with portfolio managers and quants to translate research into scalable strategies.
• Contribute to ongoing improvement of the investment process.

Skills

Quantitative research

Alpha generation

Signal development

Backtesting

Statistical modelling

Education

Bachelor's degree or higher in Mathematics, Statistics, Physics, Computer Science, Engineering or related quantitative discipline

Tools

Python

Job description

Radley James in London is seeking a Quantitative Researcher for a mid-frequency systematic equities pod in a leading global hedge fund. You will perform end-to-end alpha research, including signal development, data analysis, backtesting and evaluation of predictive signals, collaborating with the PM and quants to translate research into robust strategies.

The role requires strong programming skills (Python or similar) and a quantitative degree, with a hybrid working model and competitive