Quant Researcher β€” On-Site London, Trading Systems & ML

Quant Researcher β€” On-Site London, Trading Systems & ML

Full-Time 63000 - 77000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Influence trading engines and build predictive models for market analysis.
  • Company: Good Markets, a leading firm in London with a focus on innovation.
  • Benefits: Competitive salary, hands-on experience, and collaboration with founders.
  • Other info: Dynamic on-site environment with opportunities for professional growth.
  • Why this job: Make a real impact in trading systems while working with cutting-edge technology.
  • Qualifications: PhD in a relevant field, strong maths skills, and proficiency in Python or C++.

The predicted salary is between 63000 - 77000 Β£ per year.

Good Markets in London seeks a Quant Researcher with a Ph D to influence trading engines, volatility models, and risk controls, working on-site with the founders.

You'll build predictive models for volatility, regime detection, and market structure, run large-scale simulations, and contribute to automated execution improvements.

Strong mathematical background and proficiency in Python or C++ are essential; experience with ML for time-series is a plus.

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Quant Researcher β€” On-Site London, Trading Systems & ML employer: Good Markets

Good Markets in London is an exceptional employer that fosters a collaborative and innovative work culture, where your contributions directly impact trading systems and risk controls. With a focus on employee growth, we offer opportunities to work closely with industry leaders and engage in cutting-edge research, all while enjoying the vibrant atmosphere of London. Join us to be part of a team that values creativity and excellence in the fast-paced world of finance.

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Contact Details:

Good Markets Recruitment Team

We think you need these skills to ace Quant Researcher β€” On-Site London, Trading Systems & ML

PhD in a relevant field
Mathematical Background
Proficiency in Python
Proficiency in C++
Machine Learning for Time-Series
Predictive Modelling
Volatility Modelling