• Providing innovative, thorough and practical solutions to an extensive range of demanding and complicated problems

• Developing and validating loss forecasting models, conducting research on enhancements to the existing models, and applying industry best practices and techniques to meet various business needs

• 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 expert
• Working under limited direction, independently determining and developing approach to solutions

Qualifications:

• Doctorate degree (or Master's degree with equivalent work experience) in quantitative finance, statistics, economics or a related quantitative field
• Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets. Qualifying coursework may include--but is not limited to-statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods
• Coursework or work 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, and MATLAB
• Experience in model development including logistic regression is preferred
• Experience working with mortgage or consumer credit risk models, prepayment models and severity models
• Experience with competing-risk hazard models, transition models, loss forecasting and stress testing
• Strong programming skills in SAS, Python, SQL and Unix
• Experience with programming language such as R, Matlab, VBA, Java, C++
• Experience working with large data sets and relational database
• Experience in data science, machine learning and big data related technologies such as Spark and Hadoop

Keys to Success in this Role:

• Exceptional quantitative, empirical analysis, and research skills

• Strong knowledge of econometric models, tools and techniques

• Strong programming skills

Current Freddie Mac employees please apply through the internal career site.

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.

Time-type:Full time

FLSA Status:Exempt

Freddie Mac offers a comprehensive total rewards package to include competitive compensation and market-leading benefit programs. Information on these benefit programs is available on our Careers site.

This position has an annualized market-based salary range of $118,000 - $178,000 and is eligible to participate in the annual incentive program. The final salary offered will generally fall within this range and is dependent on various factors including but not limited to the responsibilities of the position, experience, skill set, internal pay equity and other relevant qualifications of the applicant.