Head of Mortgage Modeling and Analytics
Summary: A highly successful alternative asset manager is seeking a Head of Mortgage modeling and analytics to lead its internal analytics team and investment decisions across residential mortgages, whole loans, securitized products, and structured credit strategies. This team is known for being a major catalyst in the U.S. residential mortgage loan space and mission to generate long-term returns. They are looking for an experienced candidate who will work closely with the Firm's Founder and CIO to manage and develop the Modeling / analytics department from both an infrastructure and team perspective. The role provides significant visibility, investment influence, and leadership opportunity.
Key responsibilities:
- Lead all mortgage modeling, valuation, and analytics initiatives across the platform.
- Develop and enhance prepayment, default, severity, and cash flow models using large-scale loan-level datasets.
- Build analytical frameworks for stress testing, scenario analysis, model validation, and performance monitoring.
- Maintain and improve pricing and valuation models across mortgage and structured credit investments.
- Analyze borrower behavior, housing market trends, macroeconomic developments, and regulatory changes that impact investment performance.
Job requirements:
- 10+ years of experience in mortgage analytics, RMBS, whole loans, structured products, or mortgage investing.
- Deep understanding of residential mortgage markets, securitization, and structured credit.
- Strong quantitative background with experience developing behavioral mortgage models and valuation frameworks.
- Expertise in Python and large-scale data analysis.
- Experience leveraging machine learning, AI frameworks, or advanced statistical techniques is highly desirable.
- Prior experience working directly with Portfolio Managers, Traders, or Investment Committees.
- Proven leadership experience managing teams and analytical functions.
- Advanced degree in a quantitative discipline such as Finance, Statistics, Economics, Mathematics, Physics, Computer Science, or Engineering preferred.
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