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Quantitative Researcher

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Winston Fox
Full Time position
Listed on 2026-06-24
Job specializations:
  • Research/Development
    Data Scientist, Mathematics, Artificial Intelligence
Salary/Wage Range or Industry Benchmark: 80000 - 100000 GBP Yearly GBP 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Location: Greater London

Unique opportunity to join one of the most famous and successful High Frequency Trading teams in the world. This role reports directly to the head of this team, who is regarded as one of the greatest quants and traders in a generation.

You do not need to have financial experience, but their expectation is that you will be one of the top 0.1% of Mathematical and Machine Learning researchers in the world, with all the associated accolades to back up your expertise

This team's focus is on Mathematical modelling excellence and cutting-edge Machine Learning research. We are looking for a candidate with a passion for mathematical research and depth, but with an obsession with making sophisticated models work in the real world. R

  • ole Designing and developing deep learning models for time-series and signal predicti on.

    Making models more robust and optimi sed Working with technologists to turn research into production syste ms.

    Learning about the markets and market structure to achieve better modelling outcomes and succe ss.

    This role is not applying basic methods to clean data. This is real ML research on challenging d
ataNe
  • ededMSc or PhD research experience, published in the top journals and highly cited

    Evidence of novel ability in Maths, Stats, Machine Learning or Computer Science, i.e., Publications, industrial research, open-source contribution s...Experience designing and developing large models. Superb time-series modelling, reinforcement learning, causal inference, probabilistic modelling, optimisation, etc Exposure to taking your research beyond a prototype is highly desira ble.

    Deep understanding of modern deep learning: architectures, objectives, optimisation, scaling, evaluation and failure mo des.

    Exceptional mathematical and statistical grouding

    Excellent programming - any language (demonstra
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