Solving high-impact problems at the intersection of data, algorithms, and markets at Susquehanna
Requirements
- Proven experience applying machine learning techniques in a professional or academic setting
- Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow
- Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR
Responsibilities
- Conduct research and develop ML models to identify patterns in noisy, non-stationary data
- Work side-by-side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting-edge ML research and practical implementation
- Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches
- Design and run experiments using the latest ML tools and frameworks
- Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior
- Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk-aware decision making
- Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices
Other
- Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field
- Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment
- Collaborative, intellectually stimulating environment with global reach
