Quantitative Analysis and Research Intern

Overview:

The Quantitative Analysis and Research Intern will support the Investments team by helping model investment data to improve internal analysis tools and measure investment performance. This role focuses on applying statistical methods to enhance decision-making related to performance, support operational reporting, and strengthen internal processes. The intern will gain hands-on experience applying statistical techniques to real-world investment data while contributing to projects that improve transparency and operational insights across the team and to our many external stakeholders.

About Texas Permanent School Fund (PSF) Corporation:

The Texas PSF Corporation is a special-purpose governmental corporation responsible for managing and investing the $57+ billion Permanent School Fund to support public education across Texas. Our team is based in Austin, Texas, working in a dynamic, collaborative environment with a strong mission-driven focus. The Corporation's investment strategies are designed to deliver strong, risk-adjusted returns while safeguarding the future of Texas public education.

Key Responsibilities:

• Clean, organize, and prepare historical investment data for analysis
• Apply sampling techniques (such as bootstrapping) to investment-related datasets
• Explore fitting probability distributions (Normal, Skewed Normal, Gumbel, etc.) to historical data
• Conduct statistical tests (such as the Kolmogorov-Smirnov test) to evaluate modeling approaches
• Develop models and templates to improve internal tracking and measurement of investment performance
• Analyze investment-related performance metrics (e.g., Time-Weighted Return, IRR, Multiples) to support reporting consistency and mission alignment
• Summarize technical findings and assist the team in building internal reporting tools
• Collaborate with Performance Committee members across various investment strategies

Preferred Technical Skills and Concepts:

• Experience in data analysis, statistical and financial modeling
• Exposure to Python, R, MATLAB, VBA, and strong proficiency in Excel
• Understanding of complex probability distributions and resampling techniques
• Familiarity with investment-related metrics (TWR, IRR, multiples)

Bonus Skills (not required, but a plus!):

• CFA, CAIA, or equivalent candidate
• Practical experience with hypothesis testing and goodness-of-fit testing
• Interest in designing analytical templates and reporting tools

Ideal Candidate Profile:

• Pursuing a Bachelor's or Master’s degree in Finance, Economics, Statistics, Mathematics, Data Science, or a related field
• Strong interest in investments, quantitative analysis, and applied data work
• Curiosity and willingness to independently learn new statistical modeling techniques
• Ability to work independently on technical projects and communicate findings clearly and concisely
• Comfortable working with large, complex, or unstructured datasets