Quantitative Analytics Senior

Freddie Mac•McLean, VA

1d•$126,000 - $190,000

About The Position

As a Data Scientist on Freddie Mac’s Collateral Modeling and Analytics team, you will use data science to address business problems and develop tools used in daily decisions about collateral underlying mortgages in Freddie Mac’s portfolio. You will apply statistical and machine-learning techniques, distributed computing technologies, and advanced modeling algorithms to structured and unstructured big data. The work supports intelligent business decisions and risk management. The role develops and implements collateral-risk models for new and existing financial and mortgage products. It includes prototyping and coding models, modifying existing application source code, coordinating implementation testing, conducting back tests, and performing economic and stress tests. You will provide technical modeling support to a business or product area, document model development and rationale for oversight and review, and independently resolve problems under limited direction. Freddie Mac emphasizes employee responsibility for following acceptable-use, privacy, security, and regulatory policies and procedures. The position is full-time and exempt, with an annualized market-based salary range of $126,000 to $190,000 and eligibility for the annual incentive program. Freddie Mac describes its total rewards package as including competitive compensation and benefit programs; details are available on its Careers site.

Requirements

Doctorate degree, or a master’s degree with three 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 datasets.
Coursework or work experience writing statistical or optimization programs to develop models and algorithms.
Experience working with large datasets and relational databases.
Strong oral and written communication skills, including the ability to explain complex technical scenarios in clear, simple terms.
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 ability to learn new big-data and machine-learning technologies.
Demonstrated knowledge of AI tools and technologies, with commitment to continuous learning and keeping up with AI advancements.

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, or image analytics.
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.
Programming languages may include Python, R, SAS, SQL, Java, or MATLAB.

Responsibilities

Develop analytical methods and models to 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 as a day-to-day technical specialist.
Document technical analytics and rationale throughout model development to support model oversight and review for approval.
Independently resolve problems and develop approaches to solutions under limited direction.
Follow applicable policies and procedures and meet privacy, security, and regulatory obligations.