Heading
New York, NY
Internship
Onsite
$60,000/wk - $60,000/wk
Intern
Susquehanna International Group is offering a 10-week immersive Machine Learning PhD Internship for candidates passionate about solving high-impact problems at the intersection of data, algorithms, and markets. Interns will work on real-world financial problems, applying machine learning and data science techniques while collaborating with the research team to improve existing models and explore new algorithmic approaches.
Apply NowResponsibilities
- 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
- One-on-one mentorship from experienced researchers and technologists
- Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices
- 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
Qualification
Required
- Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field
- Proven experience applying machine learning techniques in a professional or academic setting
- Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR
- Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow
- Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment
Preferred
Benefits
- Signing bonus
- Housing
- Breakfast and lunch
- Other perks