Quantitative Analytics Professional

Freddie Mac•McLean, VA

5d•$105,000 - $157,000

About The Position

Freddie Mac’s Single-Family Modeling team is seeking a Quantitative Analytics Professional to support our growing AI enablement efforts. This role contributes to our technology modernization roadmap, with a focus on advancing the adoption of GenAI capabilities across the organization. The individual will partner closely with modeling teams and cross-functional stakeholders to identify, evaluate, and scale practical GenAI use cases, improving efficiency while maintaining strong governance. The individual will also support advanced research initiatives as needed, contributing to experimentation, prototyping, and model development. Freddie Mac is leading the U.S housing market forward by making homeownership and rental housing more accessible and affordable. Our team develops robust, scalable models that support the Single-Family business by enabling critical, data-driven risk decisions.

Requirements

Advanced degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering); Ph.D. preferred
Proficiency in Python, SQL, and Spark
Proficiency in ML frameworks (e.g., Scikit-learn, PyTorch, TensorFlow)
Familiarity with LLMs and GenAI applications, including prompt design, evaluation, and agentic workflows
Experience with end-to-end model development workflows (experience in financial services or other regulated environments is a plus)
Strong problem-solving skills, with the ability to manage multiple priorities in a fast-paced environment
Excellent written and verbal communication skills, with the ability to convey technical concepts to diverse audiences

Nice To Haves

Experience in financial services or other regulated environments

Responsibilities

Partner with modeling teams and stakeholders to identify and enable AI capabilities aligned with business objectives, regulatory requirements, and enterprise risk management frameworks.
Contribute to the adoption of effective tools and practices for model development, version control, and reproducibility. Support efforts to improve consistency, transparency, and quality across teams.
Stay current on emerging AI technologies and apply established methods and tools to support the modernization of end-to-end model pipelines and analytics on large-scale datasets.