Quantitative Analytics Senior Tech Lead - 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: Are you curious, entrepreneurial, and analytical? Do you enjoy solving complex real-world problems with advanced quantitative techniques? Freddie Mac’s Multifamily Modeling team within Multifamily Portfolio and Risk Management is seeking a Quantitative Analytics Senior Tech Lead to develop and manage quantitative models, deliver analytics, and create methodologies that support decision-making and risk management within the $500Bn+ Multifamily business. You will lead projects, coordinate with stakeholders, and work independently to conduct data analysis, model development, prototyping, testing, and cross-team collaboration. Ideal candidates have strong mathematical/statistical modeling expertise and robust programming skills, with experience in time series econometrics or large-scale simulation models (e.g., Monte Carlo simulations, multi-scenario forecasting engines, cash-flow simulators). Our Impact : Drive quantitative research and modeling for multifamily credit risk, economic capital, risk rating, and costing/pricing analytics. Innovate analytics to improve efficiency and strengthen risk management. Ensure compliance with model risk governance and regulatory standards. Your Impact: Perform advanced research to develop and enhance modeling approaches for MF credit models. Develop and implement statistical models using common programming languages. Provide analytical support across multifamily business functions. Translate model insights into actionable recommendations for senior stakeholders. Manage projects effectively and communicate progress clearly. Support model governance activities to ensure compliance. Qualifications: Advanced degree (Master’s or Ph.D.) in Economics, Finance, Statistics, Mathematics, or related field preferred; 10+ years relevant experience in quantitative modeling, mortgage credit risk, commercial real estate analytics, financial risk management, or related fields. Expertise in predictive modeling, econometrics, optimization, machine learning, and Monte Carlo simulations. Proficiency in programming languages such as Python, R, C++, SQL, or SAS. Proficiency in designing stochastic processes, modeling risk drivers, and validating simulation outputs. Experience in financial modeling, model validation, and analytics. Ability to translate business requirements into modeling and analytics solutions. Strong project management skills and ability to work independently. Knowledge of financial services and mortgage finance preferred. Keys to Success in this Role: Ownership of work, can-do attitude and collaborative mindset. Self-motivated and able to work efficiently under pressure. Innovative problem-solving and research skills. Detail-oriented while maintaining strategic perspective. Strong coding and analytical/statistical skills. Ability to justify modeling choices and present results effectively. Current Freddie Mac employees please apply through the internal career site. We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under applicable federal, state or local laws. We will ensure that individuals are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. A safe and secure environment is critical to Freddie Mac’s business. This includes employee commitment to our accepta