Machine Learning Internship - PhD: 2027 at Susquehanna… · US-NY-New…

Summary: Susquehanna International Group is a global quantitative trading firm that emphasizes scientific rigor and innovation. They are seeking a Machine Learning Intern who will work on high-impact projects applying machine learning and data science to financial problems while collaborating closely with their research team. 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 - 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 Required Qualifications: - 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 Required Skills: Machine learning, Data science, Quantitative trading, Financial modeling, PyTorch, TensorFlow, Scientific research, Statistical analysis, Signal processing, Model deployment, Risk-aware decision making, Drive to innovate Graduation Date Requirement: Graduate in 2027 Benefits: Signing bonus, Housing, Breakfast and lunch, Other perks