Quantitative Analytics Senior

Freddie Mac•McLean, VA

3d•$126,000 - $190,000

About The Position

Freddie Mac’s Investments & Capital Markets Division is seeking a Quantitative Analytics Senior to develop, implement, monitor, and execute quantitative models that support counterparty credit risk management, fixed-income derivatives valuation, and related business and risk management decisions. The candidate should be self-motivated, has a strong quantitative and computational background, and communicates effectively with technical and business stakeholders. As part of the Models & Analytics team, this role will primarily support Freddie Mac’s Counterparty Credit Risk Management and Asset-Liability Management functions, with responsibilities spanning model development, implementation, monitoring, data processes, documentation, and business user support. This role focuses on the design, development, implementation, and monitoring of quantitative models and analytics that support counterparty credit risk, exposure measurement, derivatives valuation, and related risk management activities. The models and analytics developed by the team provide key inputs into counterparty credit risk management, portfolio management, business reporting, and risk-informed decision-making across the division.

Requirements

Doctorate or Master’s degree + 3 years relevant experience in quantitative finance, economics, statistics, mathematics, or a related quantitative field.
Coursework or work experience applying finance, statistics, mathematics, data science, and computer programming techniques to quantitative modeling problems in the financial industry.
Relevant coursework may include statistics, mathematical programming, optimization, machine learning and AI, computational methods, design and analysis of algorithms, derivatives, and Monte Carlo methods.
Coursework or work experience developing models, analytics, and algorithms using programming languages and tools such as MATLAB, Python, SQL, Java, and Excel/VBA.
Experience sourcing, analyzing, validating, and reconciling large financial datasets used in model development, execution, monitoring, and risk reporting.
Familiarity with counterparty credit risk concepts, including initial margin, variation margin, PD, LGD, EAD, exposure measurement, and related regulatory requirements.
Familiarity with regression models, stochastic process modeling, and Monte Carlo simulation.
Experience with financial derivatives, valuation, risk analytics, and Greeks.

Nice To Haves

Strong quantitative, technical, research, and programming skills.
Strong analytical skills with attention to detail, data quality, and model controls.
Self-motivated and able to own projects, manage priorities, and work efficiently under tight deadlines.
Ability to understand complex business requirements, define relevant analytical problems, and translate model results into business terms.
Strong verbal and written communication skills, with the ability to collaborate effectively across technical, business, and governance teams.

Responsibilities

Develop, implement, and maintain quantitative models primarily for counterparty credit risk measurement, with additional coverage of interest rates, derivatives valuation, and valuation components related to mortgage products.
Implement models and analytics using programming languages and tools such as MATLAB, Python, SQL, Java, and Excel/VBA.
Manage data processes that support model development, implementation, monitoring, and reporting, including data sourcing, validation, reconciliation, quality controls, and issue resolution.
Analyze large financial datasets, including market, trade, counterparty, collateral, margin, and reference data used in risk analytics.
Design and execute model monitoring plans, produce performance monitoring reports, and respond to questions from business users, model validators, and other stakeholders.
Prepare detailed model documentation and technical documentation for internally developed and vendor models in accordance with model risk standards.
Support business users by monitoring model use and performance, producing business-line reports, and explaining model analytics in clear business terms.
Collaborate with Counterparty Credit Risk Management, model governance, model validation, technology, and other stakeholders to support model implementation, controls, and ongoing use.
Develop practical solutions to complex business problems and support the implementation and validation of business strategies.
Proactively partner with teammates and business users to develop practical analytical approaches and advance new ideas.