Quantitative Researcher - Systematic Equities

Location: London

A leading global investment firm is seeking a Quantitative Researcher to join its Systematic Equities team. Working closely with a Senior Portfolio Manager, you'll be responsible for researching and developing alpha-generating strategies using a combination of statistical techniques, machine learning and large-scale data analysis.

This is an opportunity to work in a highly collaborative environment where research is taken from concept through to live implementation and has a direct impact on portfolio performance.

What You'll Be Doing

• Generate and evaluate new alpha ideas for systematic equity strategies.
• Research, clean and analyse a wide range of structured and alternative datasets.
• Design, test and refine predictive models using statistical and machine learning techniques.
• Build robust research infrastructure and analytical tools in Python.
• Perform rigorous backtesting and validation of trading signals.
• Collaborate closely with the Portfolio Manager throughout the research and investment process.
• Write high-quality, maintainable code and contribute to a shared research framework.

What We're Looking For

• Master's or PhD in a quantitative discipline such as Mathematics, Physics, Statistics, Computer Science or Engineering.
• Strong Python programming skills and experience building quantitative research tools.
• Excellent analytical ability with a scientific, hypothesis-driven approach to problem solving.
• Strong communication skills and the ability to work effectively in a collaborative research environment.

Desired Experience

• At least three years' experience researching systematic equity strategies.
• Proven track record developing and testing equity alpha signals.
• Experience working with intraday equity data and quantitative trading models.
• Familiarity with statistical learning techniques and predictive modelling.

Additional Experience of Interest

• Research involving alternative datasets or fundamental data.
• Statistical arbitrage or market-neutral equity strategies.
• Machine learning applications within quantitative investing.
• Commercial mindset with strong intuition for identifying robust investment opportunities.