Quantitative Researcher MFT - Equity stat-arb
- Research and develop alpha signals for short-term systematic trading strategies with holding periods typically ranging from 1 to 5 days.
- Design and implement statistical arbitrage models using large financial datasets and advanced quantitative techniques.
- Conduct rigorous empirical research, including feature engineering, signal evaluation, portfolio construction, and risk analysis.
- Develop predictive models using statistical methods and machine learning techniques where appropriate.
- Build and enhance research infrastructure used for data analysis, simulation, and strategy development.
- Perform robust backtesting and validation to assess signal stability, capacity, transaction cost impact, and out-of-sample performance.
- Monitor live strategy performance and conduct attribution analysis to understand drivers of returns and evolving market dynamics.
- Collaborate with quantitative researchers, traders, and engineers to transition research into production environments.
- Continuously identify new datasets, methodologies, and research directions to improve performance and expand alpha opportunities.
- Advanced degree in a quantitative discipline such as Mathematics, Statistics, Physics, Computer Science, Engineering, or a related field.
- 5+ years of experience in quantitative research, systematic trading, statistical arbitrage, or alpha signal development.
- Strong understanding of financial markets, market microstructure, and systematic investment processes.
- Expertise in statistical modeling, time-series analysis, hypothesis testing, and predictive analytics.
- Demonstrated experience developing and evaluating alpha signals in low signal-to-noise environments.
- Strong programming skills in Python and experience with common data science and machine learning libraries.
- Ability to conduct independent research and translate quantitative insights into implementable trading strategies.
- Excellent analytical, communication, and problem-solving skills.
- PhD or equivalent research experience in a quantitative field.
- Experience researching short-term systematic strategies, including statistical arbitrage, cross-sectional modeling, and relative-value approaches.
- Familiarity with machine learning applications for alpha generation and forecasting.
- Experience working with large-scale datasets and distributed research environments.
- Demonstrated track record of developing predictive signals that have contributed to live trading performance.
- Understanding of portfolio optimization, execution considerations, transaction cost modeling, and capacity analysis.
- Alpha signal discovery
- Statistical arbitrage research
- Cross-sectional and time-series modeling
- Short-term forecasting (1-5 day horizon)
- Portfolio construction and risk management
- Machine learning for financial prediction
- Alternative and market data research
- Systematic market-taking strategies
- Performance attribution and strategy enhancement
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