Quantitative Researcher Systematic Hedge Fund (London)

We are partnering with a leading quantitative investment firm that applies advanced machine learning and data science to global financial markets. The team is looking to hire a Senior Quantitative Researcher with proven professional experience applying advanced machine learning techniques to quantitative research, particularly in developing predictive signals and systematic trading models from complex datasets.

This role sits at the intersection of machine learning, large-scale data modelling, and quantitative finance, with a focus on researchers who have already demonstrated the ability to apply state-of-the-art ML methods in a professional setting to generate robust, data-driven insights. Researchers are encouraged to innovate, explore emerging ML methodologies, and translate theoretical research into practical trading strategies.

Key Responsibilities

1. Lead research initiatives applying advanced machine learning techniques to identify predictive signals across financial and alternative datasets.
2. Design and develop systematic trading models across multiple asset classes using data-driven approaches.
3. Explore state-of-the-art ML architectures including deep learning, reinforcement learning, probabilistic modelling, and NLP to enhance signal generation and model robustness.
4. Work closely with engineers and portfolio managers to transition research prototypes into production trading systems.
5. Communicate research findings clearly to both technical and investment teams, helping shape the broader research agenda.
6. Contribute to the firm’s research culture and mentor junior researchers.

Requirements

1. Proven professional experience applying advanced machine learning techniques in quantitative research, particularly in building predictive models or signals from large datasets.
2. PhD in Computer Science, Applied Mathematics, Statistics, Physics, Engineering, or another quantitative discipline.
3. Strong expertise in machine learning and statistical modelling, including supervised, unsupervised, and reinforcement learning techniques.
4. Experience building end-to-end ML pipelines, including feature engineering, model validation, and large-scale experimentation.
5. Strong programming skills in Python, with experience using frameworks such as PyTorch, TensorFlow, or JAX.
6. Experience working with large, noisy, or high-dimensional datasets. Experience in financial markets or trading is beneficial but not required.
7. Strong problem-solving ability, intellectual curiosity, and the ability to work in a collaborative, research-driven environment.

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] .