Mortgage Quantitative C++ Modeler

A New York-based financial firm specializing in mortgage-backed securities and other asset-backed products is seeking Quantitative Modelers with strong statistical backgrounds and experience implementing mortgage prepayment models, mortgage default models, and derivative valuation models used to support a robust mortgage origination and mortgage investment portfolio.

Responsibilities

• Develop, enhance, and implement Prepayment and default models in the RMBS/ABS/Consumer Lending space.
• Develop loan origination and pricing models.
• Develop production quality ETL and data integrity processes to build and maintain credit models.
• Create visual tools for monitoring and adjusting model performance.
• Develop tools to run and analyze bid lists, dealer offerings, and new issue deals in the structured credit space.
• This is a hybrid modeling/development role.

Requirements

• Advanced degree in Statistics/Data Science, Computer Science, Mathematics, or Financial Engineering from a top university
• Strong software engineering skills in C++ Coding, Java, Python
• Experience with Mortgage Vendor Software products: ADCo, Intex, Polypaths, QRM, Yieldbook, Bloomberg
• 4+ years of Statistical Quantitative Modeling experience in the mortgage space
• Experience with mortgage credit modeling (prepayments, defaults, severities)
• Experience with statistical models such as linear and non-linear regression, logistic regression, and generalized linear models.
• 4+ years of Industry exposure to fixed-income valuation and risk management. Exposure to structured credit (RMBS, CLOs, CMBS, ABS)
• Self-motivated, organized, and driven individual with excellent communication skills.

Base Salary Range : $150,000 - $200,000. This represents the presently-anticipated low and high end of the Company’s base salary range for this position. The actual base salary range may vary based on various factors, including but not limited to location and experience.

Total Direct Compensation : This job is also eligible for discretionary bonus and incentive compensation on an annual basis that can increase total compensation 30%-50%.

Key Words: C++, Python, Credit, Modeling, RMBS, CLOs, CMBS, ABS

Please send resumes to Jim Geiger