Quantitative Researcher

Our client is a $28bn global multi-strategy hedge fund, widely recognized for its collaborative internal culture, exceptional performance track record, and commitment to developing talent from within. The firm is known for empowering its researchers with autonomy, providing state-of-the-art infrastructure, and offering clear pathways to Portfolio Manager (PM) promotion for those demonstrating strong and consistent performance.The OpportunityThe fund is expanding its stat-arb capabilities across multiple global offices and is seeking Senior Quantitative Researchers to join high-performing pods led by established Portfolio Managers in the USA, London, and Dubai.Successful candidates will play a key role in developing and enhancing short- to medium-horizon systematic strategies across global equities and futures, contributing to the continued evolution of the fund’s alpha generation and risk management frameworks.Key ResponsibilitiesResearch, design, and implement systematic trading strategies within a stat arb framework.Analyze large and complex datasets to identify statistically significant and economically meaningful trading signals.Collaborate closely with PMs, data engineers, and technologists to deploy models into production and continuously optimize performance.Conduct rigorous alpha research, signal validation, and portfolio construction analysis.Contribute to the broader research agenda through innovation in alternative data utilization, feature engineering, and signal robustness testing.Support ongoing enhancement of risk models and execution algorithms.Required Experience & Qualifications3+ years of experience in quantitative research within a leading hedge fund, proprietary trading firm, or Tier 1 investment bank.Proven track record in Statistical Arbitrage, Equities, or Futures research.Strong academic credentials in a quantitative discipline (e.g., Mathematics, Physics, Computer Science, Statistics, Engineering).Deep understanding of time-series analysis, machine learning, and predictive modelling techniques.Proficiency in Python, C++, or other modern programming languages used in systematic research.Strong grasp of market microstructure and experience working with high-frequency and alternative datasets is highly desirable.Excellent communication skills and a collaborative approach to working within multi-disciplinary teams.For more information contact David McMahon David@pointonetalent.com