Quantitative Researcher
5 days ago
Equity Stat Arb Quant Researcher – Systematic Strategies – Up to $1.5M
We are seeking an experienced Quantitative Researcher to join a high-performing team focused on fully systematic equity stat arb strategies. This role offers the opportunity to work in a dynamic, fast-paced environment where you will contribute to the development of cutting-edge quantitative models that drive high-alpha returns.
Key Responsibilities:
- Strategy Development: Design and implement fully systematic, high Sharpe equity stat arb strategies using advanced quantitative techniques.
- Alpha Research: Conduct in-depth research to identify new alpha signals, leveraging a vast range of datasets and statistical methods. Focus on discovering unique insights that can be integrated into live trading models.
- Market Data Analysis: Analyze large, complex datasets to extract meaningful patterns and predict asset price movements. Apply machine learning and advanced data analysis techniques to enhance model performance.
- Portfolio Optimization: Develop robust risk management frameworks and optimize portfolio construction methodologies, balancing risk and return with precision.
- Back-Testing and Performance Evaluation: Rigorously back-test and stress-test strategies across various market conditions, refining and enhancing models to adapt to evolving markets.
Required Skills and Qualifications:
- Strong expertise in developing fully systematic equity stat arb strategies with a successful track record of deployment.
- Deep understanding of quantitative finance concepts, including time-series analysis, statistical modeling, and portfolio optimization.
- Exceptional programming skills in Python and C++, with the ability to handle large-scale data efficiently.
- Advanced degree (Ph.D. or Master’s) in a quantitative discipline such as Mathematics, Statistics, Computer Science, or Engineering.
Desired:
- Experience working within a high-frequency trading (HFT) environment or applying low-latency techniques is highly valued.
- Familiarity with machine learning frameworks and data-driven research methodologies is a plus.
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