Position Overview We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches
Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk
Design and implement analytical tools and pipelines using Python and SQL
Contribute to model validation, backtesting, and performance evaluation
Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure
Communicate complex quantitative insights through clear visualizations and technical summaries
Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics
Required Qualifications
Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines)
Strong foundation in probability, statistics, and numerical methods
Proficiency in Python (NumPy, pandas, or similar) and/or SQL
Experience working with large datasets and implementing quantitative models
Ability to think rigorously about complex systems and translate theory into practical solutions