Graduate Machine Learning Researcher - Chicago
Core
Design, implement, and evaluate machine learning models for global trading strategies across equities, futures, and options markets.
Role type
PhD-level machine learning researcher (quantitative finance)
Builds
Production trading systems and novel ML approaches for global markets
Domain
Quantitative finance / Financial markets
Deliverable
production ML models
Required skills
Deep learning fundamentals, neural network architectures, sequence modeling, training dynamics, optimization, statistical analysis, model risk assessment, Python, PyTorch, TensorFlow, JAX
Preferred skills
Publications at NeurIPS, ICML, ICLR, research internships
Responsibilities
Design and evaluate ML models for trading, apply statistical techniques to measure performance and control overfitting, investigate novel ML approaches, communicate research progress, collaborate with traders and engineers to translate research into production systems
Seniority
PhD-level researcher