Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary) 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.

• 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

• 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 (Num Py, 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

• Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk)
• Experience with scientific computing, optimization, or machine learning
• Background or research in cross-disciplinary areas such as:
  • Statistical physics, complex systems, or network theory
  • Applied or computational mathematics
  • Machine learning or probabilistic modeling
  • Quantum computing or advanced optimization techniques
  • Topological data analysis or geometric data methods
• Prior research, publications, or project work demonstrating advanced quantitative modeling

• Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics
• Opportunity to apply cutting‑edge academic methods in a production environment
• Collaboration with a highly quantitative, cross‑disciplinary team
• Experience working with large-scale financial data and modern analytics infrastructure
• Mentorship and potential pathway to full-time quantitative roles

• Internship:
  Summer 2026, with potential to extend
• Paid internship (competitive, based on experience and location)