At Freddie Mac, the business problem is to apply quantitative analytics to influence business strategy and decision making in the Single-Family Mortgage domain, with the goal of making home possible for more families across the country.
Requirements
- Fluency in SAS/Python/SQL and prior experience building or implementing models/analytical frameworks in SAS/Python/R scripts.
- Proficiency in programming languages such as SQL (required), Python (required), SAS (good to have).
- Strong experience with Tableau, PowerPoint and Excel
- Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets.
- 5+ years of experience writing statistical or optimization programs to conduct data analytics on large data sets.
- Solid understanding of risk, credit, and the mortgage life cycle.
- Strong quantitative, analytical, and problem-solving skills.
Responsibilities
- Monitoring credit risk patterns of SF new fundings and proactively identify emerging risk by applying AI/Machine Learning techniques to advance analytics.
- Manage the GEO Intelligence Forum which stays on top of emerging macro and mortgage performance patterns across GEOs
- Apply sophisticated technical skills to provide deeper insights into credit risk trends using traditional and non-traditional mortgage data.
- Enhance analytical ability in the team by building subject matter expertise in risk models, statistical methods, credit policy, credit risk, business trends as well as technical data skills.
- Automate formulation of business rules in targeting different business objectives.
- Responsible for the development and execution of new and innovative analytics and as well as being on top of key relevant model updates.
- Apply predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets.
Other
- Doctorate degree (or Master's degree with 3 years equivalent work experience) in quantitative finance, statistics or a related quantitative field is preferred.
- Curiosity, ability to ask questions and build good relationships across the organization
- Creative and practical problem solving
- Flexibility as we develop new processes reacting to evolving regulatory landscape
- Willingness and ability to quickly pick up complicated concepts and engage subject matter experts to obtain desired inputs/results
