VP, Mid-Frequency Quantitative Trading & Research

Next Frontier Capital; New York (NY); On-site; USD 250,000 - 350,000; Full time

Job summary

JPMorgan Chase is seeking a Vice President for its Mid-Frequency Trading Strategies team in New York. You will design and implement a mid-frequency trading framework, from ideation and backtesting to live deployment and performance monitoring.

The role requires deep expertise in statistical modelling, time-series analysis, and machine learning, with strong Python skills and a track record of turning research into live alpha generation.

Qualifications

• Master's degree in a quantitative STEM field (Statistics, Mathematics, Physics, CS, or Financial Engineering).
• Minimum 5 years in quantitative trading, quantitative research, or systematic strategy development.
• Strong foundation in statistical modelling, time-series, and hypothesis testing in finance.
• Hands-on ML experience (gradient boosting, neural nets, regularization, dimensionality reduction).
• Proficiency in Python with NumPy/pandas/scikit-learn and ML frameworks.

Responsibilities

• Improve mid-frequency trading framework architecture for signal generation and execution.
• Research and develop proprietary strategies using advanced stats and ML.
• Apply ML methods to large market datasets and macro indicators.
• Own end-to-end research from hypothesis to backtesting and deployment.
• Develop production-grade models and integrate with live trading systems.
• Monitor performance, attribution, and iterate models to maintain PnL.

Skills

Statistical modelling

Time-series analysis

Machine learning

Python

Quantitative research

Education

Master's degree in Statistics/Mathematics/Computer Science/Financial Engineering

PhD in quantitative STEM discipline

Tools

NumPy

pandas

scikit-learn

PyTorch

TensorFlow

Job description

JPMorgan Chase is seeking a Vice President for its Mid-Frequency Trading Strategies team in New York. You will design and implement a mid-frequency trading framework, from ideation and backtesting to live deployment and performance monitoring.

The role requires deep expertise in statistical modelling, time-series analysis, and machine learning, with strong Python skills and a track record of turning research into live alpha generation.