Job Description - Senior Quantitative Researcher Boutique Systematic Hedge Fund | London
We are working with a leading London-based systematic hedge fund that places machine learning at the core of its investment process. The firm is expanding its research team and is looking to hire a Senior Quantitative Researcher to design, test, and deploy machine-learning-driven models used directly in live trading.
This role is suited to researchers who want to push the boundaries of applied machine learning in a real-world, high-impact environment. You will focus on extracting signal from large, complex datasets and building scalable ML systems that inform systematic trading decisions across global markets.
Unlike more traditional quant roles, this position is explicitly ML-centric, with significant freedom to experiment with modern architectures, data sources, and research methodologies, and a clear pathway from research to production.
What You’ll Be Working On
Developing machine-learning-based alpha models using financial and alternative data at scale
Applying advanced techniques such as deep learning, reinforcement learning, probabilistic models, and representation learning to market prediction problems
Designing end-to-end ML research pipelines, including feature construction, model training, validation, and performance attribution
Running large-scale experiments to evaluate new ideas and improve model robustness
Working closely with engineering and portfolio teams to transition research models into live trading systems
Communicating research results clearly and influencing the broader quantitative research agenda
Mentoring and technically guiding more junior researchers as the team grows
Background They’re Looking For
PhD (or equivalent research background) in Machine Learning, Computer Science, Statistics, Applied Mathematics, Physics, Engineering, or a related quantitative discipline
Strong hands-on experience with modern machine learning methods, including both classical and deep learning approaches
Deep understanding of model validation, overfitting control, and working with noisy, high-dimensional data
Excellent Python skills and experience with ML frameworks such as PyTorch, TensorFlow, or JAX
Proven ability to conduct independent research and turn abstract ideas into working models
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