Alexander Chapman
A leading systematic investment firm is seeking a
Quantitative Researcher – Equities & Futures
to develop and enhance alpha-generating strategies across global markets. This role is ideal for researchers with a strong foundation in statistical modeling, machine learning, and financial market dynamics, looking to work at the intersection of data science and systematic trading.
Key Responsibilities
Research, design, and backtest systematic strategies across equities and futures, leveraging both traditional and alternative datasets.
Develop predictive signals and statistical arbitrage models focused on alpha generation and portfolio optimization.
Analyze large-scale market, fundamental, and alternative datasets to identify inefficiencies and trading opportunities.
Collaborate with engineering teams to implement research pipelines and deploy models into live trading environments.
Monitor strategy performance, conduct post-trade analysis, and refine models based on market behavior and execution feedback.
Candidate Requirements
2+ years of experience in quantitative research, trading strategy development, or data science within a hedge fund, asset manager, or proprietary trading firm.
Proficiency in Python and/or C++ for research and production implementation; experience with data processing frameworks and numerical libraries (e.g., NumPy, pandas, scikit-learn).
Deep understanding of statistical modeling, time-series forecasting, and/or machine learning techniques applied to financial data.
Advanced degree (PhD or Master’s) in a quantitative field such as Finance, Statistics, Applied Mathematics, Computer Science, or Physics.
Strong grasp of market microstructure, trading dynamics, and risk-adjusted performance metrics.
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Quantitative Researcher – Equities & Futures
to develop and enhance alpha-generating strategies across global markets. This role is ideal for researchers with a strong foundation in statistical modeling, machine learning, and financial market dynamics, looking to work at the intersection of data science and systematic trading.
Key Responsibilities
Research, design, and backtest systematic strategies across equities and futures, leveraging both traditional and alternative datasets.
Develop predictive signals and statistical arbitrage models focused on alpha generation and portfolio optimization.
Analyze large-scale market, fundamental, and alternative datasets to identify inefficiencies and trading opportunities.
Collaborate with engineering teams to implement research pipelines and deploy models into live trading environments.
Monitor strategy performance, conduct post-trade analysis, and refine models based on market behavior and execution feedback.
Candidate Requirements
2+ years of experience in quantitative research, trading strategy development, or data science within a hedge fund, asset manager, or proprietary trading firm.
Proficiency in Python and/or C++ for research and production implementation; experience with data processing frameworks and numerical libraries (e.g., NumPy, pandas, scikit-learn).
Deep understanding of statistical modeling, time-series forecasting, and/or machine learning techniques applied to financial data.
Advanced degree (PhD or Master’s) in a quantitative field such as Finance, Statistics, Applied Mathematics, Computer Science, or Physics.
Strong grasp of market microstructure, trading dynamics, and risk-adjusted performance metrics.
#J-18808-Ljbffr