Quantitative Analytics Professional

Freddie Mac•McLean, VA

1d•$105,000 - $157,000

About The Position

Freddie Mac’s Single-Family Modeling team is seeking a Quantitative Analytics Professional to develop and enhance collateral models using statistical methods, machine learning, and data technologies. The role’s work informs daily decisions about mortgage collateral and supports risk management across Freddie Mac’s portfolio. The position collaborates with modeling teams and business partners to research, prototype, develop, and validate models using large-scale structured and unstructured data. It applies distributed computing, advanced machine learning, and emerging AI—including GenAI where appropriate—to improve collateral models and modernize modeling workflows, with an emphasis on business value, reproducibility, and strong governance. The team develops robust, scalable models that support the Single-Family business and enable data-driven risk decisions. The role also provides analytical support and technical expertise to business and product partners, and communicates research findings, model results, and recommendations to technical and business audiences.

Requirements

Master’s degree in quantitative finance, statistics, or a related quantitative field.
Coursework and/or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large datasets.
Coursework or experience applying statistical, machine learning, or econometric methods to large datasets.
Proficiency in Python, SQL, and Spark for predictive modeling and processing large-scale structured and unstructured data.
Strong problem-solving skills and ability to manage competing priorities in a fast-paced environment.
Excellent written and verbal communication skills for technical and business audiences.

Nice To Haves

Familiarity with LLMs and GenAI applications, including prompt design, evaluation, or agentic workflows.
Experience with relational or NoSQL databases.
Experience with end-to-end model development.
Financial services or regulated-industry experience.
Relevant coursework may include statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods/modeling.

Responsibilities

Develop and validate analytical methods and models to assess collateral risk for new and existing financial and mortgage products.
Research enhancements to existing models, conduct experiments, develop prototypes, and apply industry best practices to address business needs.
Evaluate and apply statistical, machine learning, and AI technologies, including GenAI where appropriate, to improve collateral models and modeling workflows in alignment with business objectives, regulatory requirements, and enterprise risk management frameworks.
Apply effective tools and practices for model development, version control, and reproducibility, improving consistency, transparency, and quality across modeling teams.
Use distributed computing and data-processing technologies to analyze large-scale structured and unstructured datasets and modernize end-to-end model pipelines.
Independently develop practical solutions to complex modeling problems under limited direction.
Provide analytical support and day-to-day technical expertise to business and product partners.
Collaborate across teams to translate business needs into analytical solutions.
Document and communicate research findings, model results, and recommendations to technical and business audiences.