JPMorganChase is forming a Mid-Frequency Strategies team focused on the research, development, and execution of systematic trading strategies. The group operates at the intersection of quantitative research and trading, developing strategies that span alpha generation, portfolio construction, risk management, and execution infrastructure — with statistical analysis and machine learning at the core. You will work alongside experienced traders, researchers, and technologists in a collaborative environment where research directly drives live trading decisions.
Job Summary
As a Vice President within the Mid-Frequency Trading Strategies team, you will play a central role in designing and implementing JPMorgan Chase’s mid-frequency trading framework. You will be responsible for the full lifecycle of strategy development — from ideation and statistical
research through production deployment and ongoing performance monitoring. This is a highly quantitative role requiring deep expertise in statistical modelling, machine learning, and financial markets, and is suited to someone who thrives at the boundary of research and live trading.
Job Responsibilities
- Improve the mid-frequency trading framework, including the architecture for signal generation, alpha combination, portfolio optimization, and execution logic, ensuring the platform is robust, scalable, and production-ready.
- Research and develop proprietary trading strategies using advanced statistical modelling and machine learning techniques, with a focus on identifying persistent, risk-adjusted alpha signals across relevant asset classes.
- Apply machine learning methodologies — including supervised and unsupervised learning, reinforcement learning, and time-series modelling — to extract predictive signals from large, complex datasets including market microstructure, alternative data, and macroeconomic indicators.
- Own the end-to-end research process, from hypothesis generation and backtesting through to live deployment, with rigorous statistical validation to guard against overfitting and data snooping biases.
- Develop and maintain production-grade implementations of trading strategies and supporting infrastructure, working with technology partners to integrate models into the live trading environment.
- Monitor live strategy performance, carry out PnL attribution, identify regime changes, and continuously iterate on models to maintain and improve P&L generation.
Required Qualifications, Capabilities, and Skills
- Master's degree in a quantitative STEM discipline such as Statistics,
- Mathematics, Physics, Computer Science, or Financial Engineering
- Minimum 5 years of experience in quantitative trading, quantitative research, or systematic strategy development role, ideally within a prop trading environment, hedge fund, or sell-side systematic trading desk
- Demonstrable expertise in statistical modelling, including time-series analysis, factor modelling, Bayesian inference, and hypothesis testing in a financial markets context
- Strong machine learning proficiency, with hands-on experience applying ML techniques (e.g. gradient boosting, neural networks, regularization methods, dimensionality reduction) to financial prediction problems
- Strong Python programming skills, including experience with scientific computing libraries (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)
- Strong analytical and problem-solving skills, with the ability to work independently and drive research from first principles
Preferred Qualifications, Capabilities, and Skills
- PhD in quantitative STEM discipline such as Statistics, Applied Mathematics,
- Physics, or Machine Learning, with a research track record demonstrating rigorous application of statistical or computational methods to complex, real-world problems
- 5+ years of hands-on experience in a proprietary trading environment — such as a systematic trading group, quantitative hedge fund, or prop trading desk, with direct ownership of or meaningful contribution to live strategies
- Proven track record in alpha research, including the full lifecycle of signal discovery: hypothesis generation, statistical validation, backtesting under realistic assumptions, and post-deployment performance attribution
- Strong command of machine learning techniques applied to financial prediction problems, with a demonstrated ability to critically assess model reliability, manage overfitting risk, and distinguish statistically significant signals from noise in low signal-to-noise environments
- Experienced in researching and developing mid-to-high frequency systematic strategies, with a nuanced understanding of how signal decay, turnover costs, and capacity constraints interact with strategy design at different frequency horizons
- Experience with cloud-based data and compute infrastructure, particularly AWS, for large-scale data processing, model training, and research pipeline automation