We are seeking Machine Learning / Deep Learning focus Quantitative Researchers to join a leading global hedge fund.
This role offers the opportunity to design, develop, and implement cutting-edge trading strategies using innovative ML/DL techniques to enhance profitability, risk management, and alpha generation in systematic equity and futures markets. The ideal candidate will leverage state-of-the-art models to identify trading opportunities, refine algorithms, and drive research projects from concept to production as the business expands into new markets.
Key Responsibilities
- Conduct in-depth analysis to identify and exploit trading opportunities in systematic equity, futures, or crypto markets, leveraging deep learning techniques (e.g., CNNs, RNNs, LSTMs, transformers) to improve prediction quality and forecast market dynamics.
- Develop, refine, and implement trading algorithms and quantitative models to maximize profitability across diverse market conditions, integrating ML/DL for signal synthesis, factor mining, and strategy optimization.
- Implement open-ended research projects from concept to production, continuously improving model design, tools, and infrastructure while backtesting strategies, monitoring performance metrics, and implementing proven signals.
- Explore new datasets, statistical techniques, and emerging technologies in deep learning to enhance the team’s trading edge, including volatility modeling, regime detection, and alpha factor development.
- Regularly update quantitative models and algorithms to respond to evolving market dynamics, conducting rigorous backtesting to validate strategy robustness and incorporating forecasting and machine learning methods like linear regression, neural networks, or other state-of-the-art models.
- Work closely with portfolio managers, data scientists, traders, and developers to optimize trading strategies, enhance risk management processes, and refine existing models through collaborative research.
- Effectively communicate research findings, methodologies, and results within the team and to senior stakeholders, participating in discussions on new theories in deep learning and their application to trading.
Qualifications
- Advanced degree (Master’s or Ph.D. preferred) in a quantitative field such as Mathematics, Statistics, Physics, Computer Science, Finance, Financial Engineering, or a related discipline.
- Minimum of 2 years of experience in quantitative trading or research within systematic equity, and/or futures markets; proven success in developing quantitative trading strategies or deep learning systems in industry is highly preferred.
- Exposure to high-frequency trading environments and a deep understanding of the strengths and weaknesses of CNNs, RNNs, LSTMs, transformers, and other ML/DL architectures.
- Demonstrable experience leveraging forecasting and machine learning techniques, such as linear regression, neural networks, or state-of-the-art models, with a publication record at ICML, AAAI, NeurIPS, IEEE, UAI, or equivalent being a plus.
- Strong programming skills, particularly in Python or C++ (experience with R, SQL, or MATLAB is a plus).
- Location: Open to base in US, Hong Kong or Singapore.