Freddie Mac-posted about 1 month ago
$101,000 - $151,000/Yr
Full-time • Mid Level
Mclean, VA
5,001-10,000 employees
Credit Intermediation and Related Activities

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 looking for an opportunity to put your technical skills to use working on advanced modeling challenges? Freddie Mac's Single-Family Modeling team is currently seeking a Quantitative Analytics Professional who will be responsible for the development and execution of quantitative analytics and statistical models to support business and risk management decisions as a member of the Model Strategy team. Apply now and learn why there's #MoreAtFreddieMac! Our Impact: Our team is responsible for accelerating the development of models across the Single Family Modeling department especially in the context of credit risk and credit summarization. Your Impact: Use statistical, machine learning and deep learning techniques to develop models, model performance monitoring reports and model implementation. Make efficient use of distributed computing technologies including Hadoop, Spark, HIVE and others to utilize structured and unstructured big data available. Collect, manipulate, and analyze large volumes of data from a variety of sources and structures. Research latest and emerging techniques and create proof-of-concept projects showcasing benefits and improvements. Design rich data visualizations to communicate and present complex ideas. Present research results to both technical and non-technical audiences. Write documentation to explain modeling techniques and analytical decisions and comply with model oversight. Develop methodologies for data validation and data quality control.

  • Use statistical, machine learning and deep learning techniques to develop models, model performance monitoring reports and model implementation.
  • Make efficient use of distributed computing technologies including Hadoop, Spark, HIVE and others to utilize structured and unstructured big data available.
  • Collect, manipulate, and analyze large volumes of data from a variety of sources and structures.
  • Research latest and emerging techniques and create proof-of-concept projects showcasing benefits and improvements.
  • Design rich data visualizations to communicate and present complex ideas. Present research results to both technical and non-technical audiences.
  • Write documentation to explain modeling techniques and analytical decisions and comply with model oversight.
  • Develop methodologies for data validation and data quality control.
  • 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 big data. Big data technologies may include-but are not limited to-Hadoop, Hive, Pig, Spark, relational databases, and NoSQL.
  • Experience using Unix, Hadoop and related technologies (Spark, Hive, etc.) as well as statistical and machine learning libraries such as scikit-learn, MLlib and Tensorflow is preferred.
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