This internship offers hands-on, project-based exposure to real-world data analytics using large-scale mortgage and housing finance datasets. Interns will work with the Single-Family Loan Performance Dataset to analyze loan origination characteristics, borrower credit profiles, portfolio performance, delinquency trends, and housing market patterns.
Participants will learn how raw real estate loan performance data can be cleaned, processed, analyzed, and transformed into meaningful insights that support credit risk analysis, portfolio monitoring, benchmark dashboards, and data-driven business decision-making.
Activities & Exposure
Work with large-scale, real-world real estate mortgage loan performance datasets.
Clean, process, and analyze loan-level and monthly reporting data using Python or R
Analyze key mortgage variables such as credit score, LTV, DTI, note rate, loan balance, delinquency status, prepayment, and modification flags
Conduct distribution analysis, geography-based analysis, vintage analysis, and portfolio risk analysis
Build interactive dashboards to visualize origination trends, underwriting metrics, loan performance, and portfolio health
Create KPI summaries, charts, maps, boxplots, and trend analyses to communicate insights clearly
Translate analytical findings into business and risk recommendations
Participate in weekly project reviews, feedback sessions, and final presentation preparation
Requirements
Preferred Background
Major in Data Science, Statistics, Computer Science, Finance, Economics, Business Analytics, or a related field
Familiarity with Python, R, SQL, Excel, Power BI, Tableau, or other analytical tools
Interest in mortgage lending, housing finance, credit risk, financial analytics, or business intelligence
Strong analytical mindset, attention to detail, and ability to explain data insights clearly
Willingness to work independently, document analytical logic, and collaborate on final project delivery