At Freddie Mac, you will do important work to build a better housing finance system and you'll be part of a team helping to make homeownership and rental housing more accessible and affordable across the nation. As part of Freddie Mac's return to the office pilot, all employees, contingent workers and visitors must be fully vaccinated against COVID-19 in order to be on-site unless they have an approved accommodation. Position Overview:
Freddie Mac's Single-Family Division is currently seeking a Quantitative Analytics Tech Lead to be responsible for the development and execution of statistical models and applications in support of business and risk decisions as a member of the House Price Modeling Team. Apply now and learn why there's #MoreAtFreddieMac!Our Impact:
Our team is responsible for the development and analytic support of the House Price Index model, House Price Forecast model, Back-end model, Counterparty Default Model, Affordable Forecast Model, and Benchmarking Default Costing model. Our models are behind many key business initiatives at Freddie Mac such as Credit Risk Transfer, Portfolio Management, Collateral Valuation, Risk Management, and Affordable Housing. We are guided and empowered by several core principles, including the belief that cross-disciplined approaches are far more likely to produce breakthrough models. We recognize that to be at our best we need to build an organization that allows individuals to learn about and participate in a variety of models and modeling techniques.
In our team, candidates work with enthusiastic and collaborative professionals with a diverse set of backgrounds in econometrics, statistics, machine learning and analytics to support and advance Innovative analytics and develop pioneering solutions to convert insights into actions. Applying their quant skills, candidates routinely extract, understand and analyze data in our wide repository of sources, and further produce insights and build models that will inform Freddie Mac's decisions on our mortgage portfolio. Candidates have the opportunity to perform a wide range of analyses with high impact on management decisions.Your Impact:
- Developing analytical methods and models that assess the credit risk of new and existing financial and mortgage products. Conducting research on enhancements to the existing models, and applying industry standard methodologies and techniques to meet various business needs.
- Providing innovative, detailed and practical solutions to an extensive range of demanding and complicated problems.
- Implementing statistical models in efficient software languages, coding model prototypes for specification and test cases, modifying source codes in the existing application.
- Coordinating the testing through the model implementation, conducting back tests to monitor the model performance, and performing economic tests and stress tests to validate the model forecast results.
- Providing modeling and analytical support to a line of business or product area, functioning as day-to-day technical specialist.
- Preparing documentation for the technical analytics and rationale through the model development to comply with model oversight and support model review for approval.
- Working under limited direction, independently resolving and developing approach to solutions.
Keys to Success in this Role:
- Doctorate degree (or Master's degree with equivalent work experience) in quantitative finance, statistics, economics or a related quantitative field.
- 5+ years of relevant experience applying predictive modeling techniques or data analytics to large datasets is preferred.
- 3+ years of relevant experience on leading model development, or model review.
- Experience applying predictive modeling techniques from finance, statistics, mathematics, data science, machine learning, or econometrics to large data sets. Qualifying coursework may include-but is not limited to - data science, statistics, machine learning, optimization, numerical analysis, scientific programming, computational methods, supervised learning, unsupervised learning, text mining, Bayesian methods, or Monte Carlo methods.
- Experience writing statistical or optimization programs to develop models and algorithms. Programming languages may include-but are not limited to-SAS, Python, R, SQL, Java, or MATLAB
- Proficiency in programming languages such as SAS, Python, SQL or Unix
- Experience working with large data sets and relational database
- Experience in statistical model development and implementation is preferred
- Experience with software development and system setup for model application is preferred
Current Freddie Mac employees please apply through the internal career site.
- Exceptional quantitative and analytics skills
- Strong knowledge of statistical models, tools and techniques
- Strong programming skills
Today, Freddie Mac makes home possible for one in four home borrowers and is one of the largest sources of financing for multifamily housing. Join our smart, creative and dedicated team and you'll do important work for the housing finance system and make a difference in the lives of others.
We are an equal opportunity employer and value diversity and inclusion at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by applicable law. We will ensure that individuals with differing abilities 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.
Notice to External Search Firms: Freddie Mac partners with BountyJobs for contingency search business through outside firms. Resumes received outside the BountyJobs system will be considered unsolicited and Freddie Mac will not be obligated to pay a placement fee. If interested in learning more, please visit www.BountyJobs.com and register with our referral code: MAC.
Job Category:Research & Modeling