Quant Researcher

While experience in high-frequency trading, market-making, or working with top proprietary trading firms is highly valued, the team is also open to candidates from low- and medium-frequency trading backgrounds. Anyone who has researched, developed, and tested alpha strategies—particularly within equities and futures—will be of strong interest. Key Responsibilities: Alpha Research & Strategy Development: Collaborate with the research team to generate alpha by conducting modeling, data analysis, and implementing systematic trading strategies. Identify, develop, and enhance trading strategies across global markets, leveraging quantitative research and statistical insights. Data Analysis & Pattern Recognition: Analyze market data and financial time series using advanced statistical techniques to detect patterns. Build and refine predictive models to optimize trading decisions. Model Implementation & Back-testing: Construct, back-test, and improve quantitative models using systematic, data-driven approaches to validate and enhance trading strategies across multiple asset classes (equities, commodities, currencies, and fixed income). Cross-functional Collaboration: Work closely with technology teams to ensure alignment between quantitative strategies and technology infrastructure. Risk Management: Apply detailed knowledge of risk management procedures to monitor and manage risk across strategies, ensuring alignment with firm-wide risk parameters. Key Qualifications: Education: Master’s or PhD in a quantitative discipline such as Applied Mathematics, Statistics, Computer Science, Physics, or a related field. Professional Experience: Experience in high-frequency trading, market-making, or working with top proprietary trading or quantitative research firms. Candidates from low- and medium-frequency backgrounds with strong alpha research and testing experience are also encouraged to apply. 5+ years of experience in quantitative finance or a proprietary trading environment. Demonstrated expertise in data analysis, financial modeling, and statistical techniques for alpha generation. Experience with systematic trading strategies or model-driven approaches. Technical Skills: Programming: Strong proficiency in Python (especially for data analysis); experience with C++ is a plus. Machine Learning: Familiarity with ML frameworks. Data Modeling: Advanced data analysis skills and the ability to apply data-driven methods in a trading environment. Problem-Solving & Innovation: Proven ability to handle complex datasets, solve problems creatively, and independently manage projects in a high-stakes environment.