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

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

What We're Looking For

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

Desired Experience

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

Additional Experience of Interest

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