Graduate Intern – Quantitative Portfolio Risk Analytics

**Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)** **Position Overview** We are seeking an exceptional 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**. Candidates from fields such as physics, applied mathematics, statistics, engineering, computer science, 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 (Master’s or PhD) program in a highly quantitative field (e.g., Applied Mathematics, Physics, Statistics, Computer Science, Engineering, Financial Engineering, or related discipline) * 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 **Preferred Qualifications** * 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 **What You’ll Gain** * 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 **Duration \& Compensation** * Internship: Summer 2026, with potential to extend * Paid internship (competitive, based on experience and location)