Lead Quantitative Analytics & Derivatives Modeling

The Fannie Mae; Washington (District of Columbia); Presencial; USD 141.000 - 184.000; Jornada completa

Descripción de la vacante

Fannie Mae is seeking a qualified Enterprise Modeling and Analytics professional to lead the development and implementation of financial models supporting investment and capital market activities. The role emphasizes interest rate modeling, curve construction, and mortgage rate modeling within a distributed computing environment, on premise and in the cloud.

You will guide design and execution of tests, ensure model quality, and coordinate cross-functional efforts to deliver robust analytical

Formación

• 4 years of experience in quantitative modeling or related fields.
• Advanced degree in a highly quantitative field is preferred.
• Demonstrated experience building models for derivatives valuation and/or term structures.
• Knowledge of Stochastic Calculus

Responsabilidades

• Lead teams translating complex mathematical and financial modeling logic into software code.
• Design and execute test cases for modeling and analytical software applications.
• Assess model implementation quality and production control risk across multiple systems.
• Define system requirements for model and analytical applications including computing power, storage, and security.
• Recommend platforms and architecture for scalable model applications.
• Evaluate emerging technologies and best practices in model implementation.

Conocimientos

Python

Educación

Bachelor's Level Degree

Descripción del empleo

Fannie Mae is seeking a qualified Enterprise Modeling and Analytics professional to lead the development and implementation of financial models supporting investment and capital market activities. The role emphasizes interest rate modeling, curve construction, and mortgage rate modeling within a distributed computing environment, on premise and in the cloud.

You will guide design and execution of tests, ensure model quality, and coordinate cross-functional efforts to deliver robust analytical