
Quantitative Trader - Digital Assets
- Chicago, IL
- Training
- Full-time
- Undergo intensive kdb/q classroom and training
- Model, analyze, and optimize existing and new trading opportunities
- Develop and enhance tools to understand key drivers and indicators of profitability and risk
- Answer questions for traders by quantifying the quantifiable and using inferences when data is imperfect
- Master's degree in financial engineering, Financial Mathematics, Computational Finance, or another STEM-related field of study
- Graduation dates between December 2025 and June 2026
- Understanding of statistical methods and experience employing optimization libraries and statistical packages (e.g. R or MATLAB)
- Strong programming experience in Python
- Overwhelming desire to solve problems and learn about market microstructure, financial markets, and algorithmic trading
- Exposure to forecasting and data mining techniques, such as linear and non-linear regression analysis, neural networks, or support vector machines
- Prior experience with relationship databases
- Prior experience with trading competitions, competitive gaming, poker, etc.