Quantitative Analytics Sr

Freddie Mac•McLean, VA

1d•Hybrid

About The Position

At Freddie Mac, the mission of Making Home Possible is central to all work. This role involves developing and/or analyzing quantitative models to assess market, credit, and/or operational risks of new and existing financial and mortgage products or portfolios, thereby supporting business and risk decisions. Key responsibilities include developing strategies to analyze and interpret model output, providing portfolio risk assessments, and offering modeling and analytical assistance to specific lines of business or product areas, often functioning as a day-to-day technical expert. The position requires evaluating and managing risks associated with the company's models, such as those for defaults, security valuation, prepayments, and loan scoring. This may involve performing detailed model validation reviews, establishing performance thresholds, researching model approaches, and creating alternative models. The successful candidate will provide innovative, thorough, and practical solutions to a wide range of complex problems, including analyses of relative value. This requires applying technical and quantitative knowledge in applied mathematics, economics, or statistics, coupled with a strong background in computer science or econometrics. The role operates under limited direction, with the individual independently determining and developing solutions, and work is evaluated upon completion for adequacy in satisfying objectives. The incumbent will represent the organization as the principal customer contact on contracts and frequently perform a project leadership role, interacting with senior customer personnel on significant technical matters that often require coordination across organizational lines. This position may be eligible for part-time telecommuting.

Requirements

Ph.D. or foreign equivalent in statistics, mathematics, economics, finance, or a closely related quantitative discipline and demonstrated knowledge in each of the following: Economic and/or financial modeling, including modern econometric/statistics estimation techniques; Programming in SQL, SAS, R, and Matlab; and Working with large data sets.
OR Master’s degree or foreign equivalent in statistics, mathematics, economics, finance, or a closely related quantitative discipline and 3 years of experience in each of the following: Economic and/or financial modeling, including experience with modern econometric/statistics estimation techniques; Programming in SQL, SAS, R, and Matlab; and Working with large data sets.

Responsibilities

Develop and/or analyze quantitative models that assess the market, credit, and/or operational risks of new and existing financial and mortgage products or portfolios to support business and risk decisions.
Develop strategies to analyze and interpret output of models or analytic applications, which assess things, such as relative risks of each product within the portfolio.
Plan, execute, and document analysis of complex financial models.
Provide portfolio risk assessments based on findings.
Provide modeling and analytical assistance to a line of business or product area, functioning as day-to-day technical expert.
Evaluate and manage risks associated with the company's models, including models of defaults, security valuation, prepayments, loan scoring, and others.
Perform detailed model validation reviews, establish performance thresholds, research model approaches, create alternative models and other means.
Provide innovative, thorough, and practical solutions to an extensive range of demanding problems, including analyses of relative value.
Apply technical and quantitative knowledge of applied mathematics, economics, or statistics, leveraging a strong background in computer science or econometrics.
Provide resolutions to an extensive range of complicated problems.
Represent the organization as the principal customer contact on contracts and often performs project leadership role.
Interact with senior customer personnel on significant technical matters frequently requiring coordination across organizational lines.