Quantitative Researcher

A leading multi-strategy investment firm is seeking a Quantitative Researcher to join a high-performing systematic equities team. This is a front-office opportunity focused on the research, development and implementation of data-driven investment strategies across global equity markets. Working directly with senior investment professionals, the successful candidate will contribute to the full research lifecycle, from idea generation and signal discovery through to portfolio construction and live strategy analysis. The role offers significant exposure to investment decision-making within a collaborative, research-intensive environment that values innovation, intellectual curiosity and rigorous analytical thinking. Key Responsibilities • Research and develop quantitative signals and alpha factors across global equity markets. • Analyse large-scale market, fundamental and alternative datasets to identify investment opportunities. • Design, test and refine systematic investment strategies using robust quantitative methodologies. • Conduct statistical analysis and backtesting to evaluate the predictive power of signals and models. • Partner closely with Portfolio Managers to generate actionable investment insights. • Contribute to portfolio construction, risk management and performance attribution processes. • Develop and maintain research tools, analytical frameworks and data infrastructure. • Evaluate new datasets, modelling approaches and machine learning techniques to enhance research capabilities. • Monitor live strategies and investigate drivers of portfolio performance. • Collaborate with researchers, engineers and investment professionals to improve the overall investment process. Requirements • 2-8 years of experience within quantitative research, systematic equities, statistical arbitrage or a related investment strategy. • Open to exceptional PhD/Postdoctoral candidates with backgrounds in Machine Learning, AI, Statistics, Mathematics, Physics or Computer Science. • Candidates from leading technology and AI research organisations with expertise in neural networks and advanced modelling techniques are also encouraged to apply. • Experienced Quantitative Researchers with a strong academic background and track record of alpha generation within systematic equities are of particular interest. • Strong understanding of statistics, probability, data analysis and quantitative modelling. • Advanced Python programming skills and experience working with large datasets. • Experience developing and evaluating predictive signals or systematic investment strategies. • Excellent analytical, problem-solving and communication skills. For more information contact: Thomas Hennelly – [email protected] Graham Murphy – [email protected]

2-8 years of experience within quantitative research, systematic equities, statistical arbitrage or a related investment strategy. Open to exceptional PhD/Postdoctoral candidates with backgrounds in Machine Learning, AI, Statistics, Mathematics, Physics or Computer Science. Candidates from leading technology and AI research organisations with expertise in neural networks and advanced modelling techniques are also encouraged to apply. Experienced Quantitative Researchers with a strong academic background and track record of alpha generation within systematic equities are of particular interest. Strong understanding of statistics, probability, data analysis and quantitative modelling. Advanced Python programming skills and experience working with large datasets. Experience developing and evaluating predictive signals or systematic investment strategies. Excellent analytical, problem-solving and communication skills.

Research and develop quantitative signals and alpha factors across global equity markets. Analyse large-scale market, fundamental and alternative datasets to identify investment opportunities. Design, test and refine systematic investment strategies using robust quantitative methodologies. Conduct statistical analysis and backtesting to evaluate the predictive power of signals and models. Partner closely with Portfolio Managers to generate actionable investment insights. Contribute to portfolio construction, risk management and performance attribution processes. Develop and maintain research tools, analytical frameworks and data infrastructure. Evaluate new datasets, modelling approaches and machine learning techniques to enhance research capabilities. Monitor live strategies and investigate drivers of portfolio performance. Collaborate with researchers, engineers and investment professionals to improve the overall investment process.