Quantitative Analytics Senior Director - Multifamily Model Research at Freddie Mac in McLean, VA

Job description

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. Position Overview: Freddie Mac’s Multifamily Modeling team within Multifamily Portfolio and Risk Management is seeking a Quantitative Analytics Senior Director to lead the Model Research function. This leader will oversee research and development of quantitative models supporting multifamily loan acquisition, credit risk management, and property valuation. The role will lead a team responsible for tackling the most challenging modeling and research topics in the model development area, including designing the next generation of Multifamily credit risk model and developing challenger approach to current models. The team will also be responsible for providing technical guidance on addressing regulatory issues and internal model validation findings. This position requires a strong quantitative and strategic leader with rigorous foundation in statistical modeling background and deep understanding of mortgage and credit risk business. Our Impact: Our team develops models to support the overall portfolio risk management activities for Freddie Mac’s Multifamily business. Our models serve the business purposes of credit decisioning at loan origination, credit loss forecasting, and enterprise stress testing and other risk measurement activities. The team also manages model risk for Multifamily business, including both regulatory issues and internal model validation challenges. Your Impact: In this role, you will: Lead a team to perform research and develop improved modeling frameworks and methodologies for quantitative models. Lead a team to develop challenger models to existing credit risk models. Provide technical and theoretical guidance on advanced model development practices and quantitative modeling approaches. Partner closely with Credit, Underwriting, Portfolio Management, Enterprise Risk, Capital Markets, Servicing, and Model Validation teams. Support model governance, validation, audit, and regulatory activities. Drive innovation in modeling methodologies, portfolio surveillance analytics, and AI-enabled model development workflows. Qualifications : 15+ years of relevant experience in quantitative modeling, mortgage credit risk, commercial real estate analytics, financial risk management, or related fields. 8+ years of people leadership or team management experience. Advanced degree (Master’s or Ph.D.) in Economics, Finance, Statistics, Mathematics, or related field preferred; substantial relevant industry experience may be considered in lieu of an advanced degree. Strong experience developing or overseeing commercial real estate, mortgage credit risk, default, prepayment, transition, loan acquisition, or portfolio surveillance models. Strong understanding of multifamily underwriting, commercial real estate fundamentals, mortgage credit behavior, default risk drivers, and portfolio risk management concepts. Strong quantitative and analytical skills, including experience with econometric modeling, survival analysis, transition modeling, statistical modeling, or large-scale data analysis. Experience leveraging AI-enabled analytical or model development tools to improve modeling productivity, research workflows, testing, documentation, or analytical insight generation. Familiarity with modern model development operations, including reproducibility, workflow management, testing automation, implementation collaboration, or scalable analytical infrastructure. Excellent execution skills with experience in delivering complex projects Experience working with regulatory, governance, model validation, and audit stakeholders in a model risk management environment. Strong programming and analytical tool experience, including Python, R, SQL, SAS, or