Susquehanna is looking to leverage advanced machine learning techniques to uncover actionable insights from large-scale financial datasets, driving data-informed decisions in quantitative finance.
Requirements
- expertise in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
- research agenda in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
- A strong theoretical foundation in ML and a passion for solving practical, open-ended problems
- Strong programming skills (Python preferred)
- experience with ML frameworks like PyTorch, TensorFlow or Jax
Responsibilities
- Conduct applied machine learning research using large-scale, real-world financial datasets
- Develop novel modeling techniques and adapt state-of-the-art algorithms to unique challenges in quantitative finance
- Collaborate with researchers and engineers to translate theoretical insights into production-scale systems.
- Contribute to the design of robust, high-performance ML infrastructure
- Explore research directions aligned with your interests, with flexibility in scope and duration
- Evaluate ideas in an industrial setting, generating insights that may inform future academic or applied work
- Help grow our research community by fostering collaboration and leveraging your network within the ML and academic ecosystems
Other
- 12–18 month fully funded faculty fellowship
- Intellectual curiosity, adaptability, and a collaborative mindset
- While research outputs cannot be published due to the proprietary nature of our work, we aim for each faculty fellow to publish technical research papers collaboratively with their research hosts, to showcase some of the machine learning and AI innovations that they developed while in residence at Susquehanna.
