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Quantitative Researcher - Graduate: 2027 (Machine Learning)
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Описание вакансии
Текст:
TL;DR
Quantitative Researcher - Graduate: 2027 (Machine Learning) (Quantitative Trading): Developing and refining systematic trading strategies through mathematical modelling, back-testing, implementation, and monitoring with an accent on probability, statistics, stochastic optimisation, and machine learning. Focus on analysing vast datasets, uncovering actionable patterns, and solving complex quantitative problems in global markets.
Location: London, United Kingdom; work from the London city-centre office
Company
is a global quantitative trading firm using scientific research, machine learning, and technology to develop systematic trading strategies.
What you will do
- Develop and refine quantitative trading strategies.
- Conceptualise research problems and build mathematical models from large datasets.
- Apply probability, statistics, numerical analysis, stochastic optimisation, and machine learning.
- Back-test, iterate, implement, and monitor trading strategies.
- Participate in hands-on trading games, project work, and quantitative trading education.
Requirements
- Recent or upcoming research master’s or PhD graduate, or post-doctoral researcher, in a quantitative discipline.
- Research experience and a strong interest in mathematical modelling and data analysis.
- Analytical, detail-oriented, and curious approach to problem-solving.
- Clear communication skills and ability to collaborate with researchers, traders, and engineers.
- Available to join the graduate programme in June 2027.
Culture & Benefits
- Industry-focused education covering options pricing, machine learning, data analysis techniques, and game theory.
- Non-hierarchical, casual, and collaborative working culture.
- Competitive compensation package for graduates.
- London city-centre office with free onsite catering, a games room, and dedicated education space.
Hiring process
- Quantitative research positions are filled on a rolling basis, so early applications are encouraged.
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