Quantitative Analytics Senior

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

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.

Position Overview:

Are you ready for a challenge? If so, Freddie Mac’s Investments and Capital Markets Division is searching for a top-notch candidate to join our Loan Portfolio Managing and Structuring (LPMS) group. Our new hire will be responsible for developing and/or analyzing quantitative models that assess the market, credit and/or operational risks of new and existing financial and mortgage products or portfolios to support business and risk decisions. Apply now and learn why there’s #MoreAtFreddieMac!

**This position can be performed currently from a remote location in the U.S. but will require presence in a Freddie Mac office in the future.***

Our Impact:

The team is responsible for management of Freddie Mac’s Non-Performing Loan (NPL) and Re-Performing Loan (RPL) positions in the company’s retained portfolio. This includes:

• Seasoned Credit Risk Transfer (SCRT),
• Seasoned Loan Structured Transactions (SLST),
• NPL sale programs.

Your Impact:

• Write programs to clean, coordinate and analyze large volumes of data.
• Make efficient use of the latest computing technologies to take advantage of big data and efficiently conduct analyses.
• Perform exploratory analysis and research and present results to both technical and non-technical audiences.
• Visualize data to communicate complex ideas.
• Work with modeling and model implementation team in the model development process.
• Test and validate model updates and outputs,
• Using market research and transaction data, research trends in various loan pool subsets and suggest potential factor dials
• Utilize model and other corporate metrics to provide mortgage analytics for transaction economics, hedging and pricing (loans and securities).
• Perform data analysis to monitor and forecast the whole-loan portfolio managed by LPMS desk on inflow / outflow, performance, changes in trend. This will involve large and complicated data sets and require familiarity with the business to iterate and drill down to relevant results.
• Research and identify statistical portfolio and transaction trends to support new or improved products to manage interest rate and credit risks.

Qualifications:

• Doctorate degree (or Master's degree with equivalent work experience) in statistics, data science or a related quantitative field.
• Coursework or work experience applying predictive modeling techniques from data science, statistics, machine learning, and 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, and image analysis.
• Coursework or work experience writing computer programs to implement data science pipelines and predictive algorithms. Programming languages may include—but are not limited to—Python, R, SQL, Java, SAS, and MATLAB.
• Coursework or work experience using technologies for manipulating structured and unstructured data.
• A minimum of 3 years of related professional experience
• Good organizational and time management skills
• Ability to work in a fast-paced trading desk environment and ability to deal with some degree of ambiguity

Keys to Success in this Role:

• Attention to detail
• Strong work ethic
• Positive attitude
• Team player
• Self-motivated
• Great communication and interpersonal skills