PhD Quant - Selby Jennings

We are seeking an exceptional Quant Researcher to join a highly collaborative and research-driven systematic investment team. This role is ideally suited to candidates completing or recently having completed a PhD in a quantitative discipline such as Mathematics, Statistics, Computer Science, Physics, Engineering, Machine Learning, Operations Research, or a related field.

You will be responsible for identifying, researching, and developing predictive signals and systematic trading strategies across global financial markets. The successful candidate will combine strong theoretical foundations with practical programming expertise to transform complex datasets into innovative investment insights.

• Conduct original quantitative research to identify alpha-generating opportunities across global markets.
• Develop predictive models using advanced statistical techniques, machine learning methods, and data-driven approaches.
• Analyse large, structured and unstructured datasets to uncover patterns and investment signals.
• Design, implement, and evaluate systematic trading strategies through rigorous backtesting and validation frameworks.
• Collaborate closely with quantitative researchers, portfolio managers, and engineers to move research ideas into production.
• Improve existing models through ongoing performance analysis, monitoring, and refinement.
• Research and evaluate new datasets, alternative data sources, and novel modelling techniques.
• Build scalable research tools and infrastructure to support efficient strategy development.
• Present research findings and recommendations to senior stakeholders in a clear and concise manner.
• Stay at the forefront of developments in statistics, machine learning, artificial intelligence, and quantitative finance.

• PhD (completed or near completion) in Mathematics, Statistics, Computer Science, Physics, Engineering, Machine Learning, Econometrics, Operations Research, or a related quantitative discipline.
• Outstanding academic record from a leading university.
• Strong background in probability, statistics, optimisation, machine learning, or related quantitative methods.
• Excellent programming skills in Python, C++, Java, or a similar language.
• Experience working with large datasets and conducting empirical research.
• Strong analytical and problem-solving abilities.
• Ability to communicate complex concepts effectively to both technical and non-technical audiences.
• Demonstrated intellectual curiosity and a passion for solving challenging quantitative problems.

• Research experience in machine learning, deep learning, reinforcement learning, statistical modelling, time-series analysis, optimisation, or artificial intelligence.
• Publications in leading academic conferences or journals.
• Experience with distributed computing, high-performance computing, or large-scale data processing frameworks.
• Exposure to quantitative finance, systematic investing, or algorithmic trading is advantageous but not required.
• Familiarity with cloud computing, data engineering, or production-grade software development practices.

• Opportunity to work alongside world-class researchers, engineers, and investment professionals.
• Access to extensive datasets, cutting-edge technology, and substantial computational resources.
• A highly collaborative, intellectually stimulating research environment.
• Direct impact on live investment decisions and trading strategies.
• Exceptional opportunities for learning, development, and long-term career progression.
• Competitive compensation package, including performance-based incentives.