About the Role
Join our ML research team and apply machine learning techniques to solve challenging problems in quantitative finance. This internship offers a unique opportunity to work on real trading signals, risk models, and market prediction systems in a production environment.
You'll work alongside quantitative researchers and ML engineers to develop, test, and deploy machine learning models that inform trading decisions. From feature engineering on financial time series to model evaluation and interpretability, you'll gain hands-on experience with the entire ML lifecycle in finance.
We're looking for students with a strong foundation in machine learning and programming who are excited about applying these skills to financial markets. Whether you're interested in deep learning, reinforcement learning, or classical ML, there are interesting problems to tackle.
What You'll Do
- • Develop and evaluate ML models for alpha signal generation and market prediction
- • Engineer features from raw financial data including price, volume, order book, and alternative data sources
- • Implement and compare different model architectures (tree-based, neural networks, ensemble methods)
- • Apply time-series cross-validation and walk-forward analysis to prevent overfitting
- • Work on model interpretability and feature importance analysis
- • Collaborate with researchers to integrate ML signals into systematic strategies
- • Monitor model performance in live paper trading and production systems
Requirements
- • Currently pursuing a Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field
- • Strong programming skills in Python with experience in ML libraries (scikit-learn, PyTorch, TensorFlow, XGBoost)
- • Solid understanding of machine learning fundamentals (supervised/unsupervised learning, cross-validation, regularization)
- • Experience with data manipulation and analysis using Pandas and NumPy
- • Understanding of probability, statistics, and linear algebra
- • Ability to implement, debug, and optimize ML pipelines
- • Strong analytical and problem-solving skills
Nice to Have
- • Experience with time-series modeling and forecasting techniques
- • Knowledge of deep learning architectures for sequential data (LSTM, Transformers)
- • Familiarity with reinforcement learning concepts
- • Experience with MLOps tools and practices (experiment tracking, model versioning)
- • Understanding of financial markets and trading concepts
- • Prior research or projects in quantitative finance
- • Experience with distributed computing frameworks (Ray, Dask) for large-scale ML
What You'll Learn
- • How to apply ML techniques to financial data while avoiding common pitfalls like overfitting and lookahead bias
- • Best practices for time-series modeling and cross-validation in finance
- • Feature engineering techniques specific to financial markets
- • Model evaluation metrics relevant to trading (Sharpe ratio, information coefficient, turnover)
- • How ML models integrate into systematic trading strategies
Ready to Apply?
Submit your resume, transcript, and cover letter. Include links to ML projects, research papers, or GitHub repositories.
Apply NowQuick Facts
- Location
- NJ/Remote
- Type
- Internship