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 ( 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 .