Quantitative Analytics Senior

Freddie Mac•McLean, VA

2d•$126,000 - $190,000

About The Position

As a Data Scientist on Freddie Mac’s Collateral Modeling and Analytics team, this role applies statistical and machine learning techniques to solve business problems and build tools used in daily decisions about mortgage collateral in Freddie Mac’s portfolio. The position uses distributed computing technologies, advanced modeling algorithms, and structured and unstructured big data to support risk management and business decisions. The role develops analytical methods and models to assess collateral risk across new and existing financial and mortgage products. It implements statistical models in efficient software languages, supports testing and model validation, and provides technical modeling and analytical support to a business or product area. The position works under limited direction, independently resolving problems and developing approaches to solutions.

Requirements

Doctorate degree, or a master’s degree with 3 years of equivalent work experience, in statistics, computer science, economics, or a related quantitative field.
Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, or computer programming to large data sets.
Coursework or work experience writing statistical or optimization programs to develop models and algorithms.
Experience working with large data sets and relational databases.
Demonstrated knowledge of AI tools and technologies and a commitment to continuous learning about developments in artificial intelligence.
Strong oral and written communication skills, including the ability to explain complex technical scenarios clearly and succinctly.
Ability to pitch and storyboard a project.
Exceptional quantitative, empirical analysis, and research skills.
Positive and independent thinking.
Hands-on ability to handle and process large quantities of structured and unstructured data.
Strong desire and capability to learn new big data and machine learning technologies.

Nice To Haves

Experience in statistical model development and implementation.
Experience with software development and system setup for model applications.
Experience with machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, and Dask.
Experience with advanced algorithms, ensemble methods, text mining, and image analytics.
Programming experience with languages such as Python, R, SAS, SQL, Java, or MATLAB.
Relevant coursework may include advanced statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, or Monte Carlo methods.

Responsibilities

Develop analytical methods and models that assess collateral risk for new and existing financial and mortgage products.
Provide innovative, detailed, and practical solutions to demanding and complicated problems.
Implement statistical models in efficient software languages, code model prototypes for specifications and test cases, and modify source code in existing applications.
Coordinate testing through model implementation, conduct back tests to monitor model performance, and perform economic and stress tests to validate model forecasts.
Provide modeling and analytical support to a line of business or product area, serving as a day-to-day technical specialist.
Prepare technical analytics and rationale documentation throughout model development to align with model oversight and support model review and approval.
Independently resolve problems and develop approaches to solutions under limited direction.