Quantitative Researcher Machine Learning-Driven Systematic Trading Firm (London)

We’re partnering with a leading quantitative investment firm that applies advanced machine learning and data science to global markets. The team is seeking a Senior Quantitative Researcher to drive the research and development of next-generation systematic trading models powered by cutting-edge machine learning methods.

This is an opportunity to work at the frontier of machine learning, large-scale data modelling, and quantitative finance — developing models that combine rigorous statistical research with modern computational techniques. Researchers are encouraged to innovate, explore emerging ML methodologies, and translate theoretical insight into practical trading solutions.

Key Responsibilities:

• Lead research initiatives applying advanced machine learning techniques to discover predictive patterns in financial and alternative datasets.
• Design, develop, and implement systematic trading strategies across asset classes using data-driven approaches.
• Explore state-of-the-art ML architectures (e.g. deep learning, reinforcement learning, probabilistic modelling, NLP) to enhance signal generation and model robustness.
• Collaborate closely with engineers and portfolio managers to translate research prototypes into production-ready systems.
• Present research outcomes clearly to both technical and investment teams, shaping firm-wide research direction.
• Contribute to the intellectual culture of the team and mentor junior researchers.

Ideal Candidate Profile:

• PhD in Computer Science, Applied Mathematics, Statistics, Physics, Engineering, or another quantitative field (postdoctoral or publication experience advantageous).
• Deep expertise in machine learning (supervised, unsupervised, and reinforcement learning) and statistical modelling .
• Strong understanding of modern ML pipelines — from feature engineering and model validation to large-scale experimentation.
• Programming proficiency in Python (and experience with ML frameworks such as PyTorch, TensorFlow, or JAX).
• Experience applying ML to large, noisy, or high-dimensional datasets; experience in finance or trading is a plus but not required.
• Strong problem-solving ability, intellectual curiosity, and collaborative spirit in a research-oriented setting.

If you’re passionate about using advanced machine learning and data-driven research to solve complex real-world problems, we’d love to hear from you.

Please send your CV to [email protected] .