Quantitative Researcher - Systematic Trading Leading London Hedge Fund in London - eFinancialCareers

We are partnering exclusively with a high-profile systematic hedge fund in London to hire a Quantitative Researcher with a strong background in machine learning and systematic trading strategy research.

This is a unique opportunity to join a top-tier firm at the forefront of data-driven and algorithmic investing, working alongside exceptional researchers and technologists. The successful candidate will play a key role in designing and implementing next-generation trading models across global equity markets.

Key Responsibilities

• Conduct end-to-end quantitative research across large-scale financial, fundamental, and alternative datasets.
• Design, develop, and implement systematic trading strategies from idea generation through to production.
• Apply machine learning, statistical modelling, and data science techniques to develop predictive signals and portfolio optimization frameworks.
• Build and maintain production-quality code for trading and research systems; monitor live predictors and portfolios.
• Collaborate closely with researchers, engineers, and portfolio managers to refine and scale successful trading ideas.
• Present research findings clearly and effectively to both technical and investment audiences.

Ideal Candidate Profile

• PhD (or equivalent research experience) in Mathematics, Statistics, Physics, Computer Science, Engineering, or another quantitative discipline.
• Demonstrated experience in systematic trading research, with a track record of building mid-to-low frequency equity strategies showing realized Sharpe ratios above 1.0.
• Strong proficiency in Python and hands-on experience with data pipeline management, including exploratory data analysis, feature/predictor construction, and performance evaluation.
• Practical experience using machine learning (supervised, unsupervised, reinforcement learning) and optimisation techniques to automate the search for alpha-generating predictors and portfolios.
• Exposure to text-based datasets, natural language processing, and embedding-based similarity models is highly desirable.
• Highly analytical, detail-oriented, and motivated to work in a fast-paced, collaborative, and intellectually rigorous environment.

To apply, please send a copy of your CV to