Quantitative Analytics Tech Lead –Credit Risk Analytics

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. Continue your career journey where your work contributes to a greater purpose.

In this role you will advance Enterprise Risk’s assessment of Multifamily credit risk through novel use of data and analytics. As part of an analytics team, you will collaborate with colleagues to develop a data-driven approach to independent assessment of the credit risk of Freddie Mac’s Multifamily loan acquisitions. This work will leverage advanced analytics to measure past performance of the portfolio, to assess the impact of macroeconomic and other risk drivers, and quantify the risk of new business.

You will study Multifamily credit models and evaluate their use in various business applications, and you will provide feedback and effective challenge to improve Multifamily credit risk measurement and risk management across the Enterprise.

Your work supports ongoing quantitative assessment of Multifamily credit risk as well as measurement of the risk impact of key policy changes. This position requires skilled work with large data sets and statistical models in the context of multifamily lending. Working knowledge of multifamily lending is needed for this job and continued growth in multifamily domain expertise will be key to success.

In this role, you are expected to scope and manage large projects; to clearly plan and document your work; to implement quality controls; and to present insights through clear written communication.

The Financial Risk team within the Enterprise Risk Division is responsible for oversight and effective challenge of the company’s most important risks, including credit, market, and liquidity risks. Together, we:

• Establish governance, policies, and standards that define how the company manages financial risks to support safety and soundness
• Monitor and report on the risk and control profile, financial risk appetite, and performance of risk indicators and metrics against thresholds and limits
• Communicate enterprise-wide risk management issues and emerging risks and monitor effective and timely issue resolution
• Provide timely and independent oversight and effective challenge of the company’s financial risk management practices and risk‑taking activities
• Assess risk to earnings and capital across a range of scenarios
• Execute an integrated oversight plan in collaboration with Operational Risk and Compliance to support the Chief Risk Officer in providing senior management and the Board with an enterprise view of risks

As Lead – Multifamily Credit Risk Analytics, you will:

• Identify trends and emerging risks in Multifamily lending through analysis of both internal and external data and information
• Develop and maintain analytical frameworks to assess Multifamily loan performance and underwriting policies
• Conduct scenario analysis to support assessment of credit policy changes and for the ongoing support of the company’s risk appetite
• Evaluate and monitor the company’s risk appetite metrics and KRIs
• Enhance the team’s risk assessment capability by building subject matter expertise in risk models, statistical methods, machine learning and AI capabilities, credit policy, credit risk, and technical data skills
• Communicate and collaborate across teams both within the Enterprise Risk Division and with first line partners in the Multifamily business. Present analysis to senior leaders and to internal and external stakeholders

• PhD in economics, finance, statistics, or related quantitative discipline with 3+ years’ experience, or Master’s with 5+ years relevant experience.
• Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large datasets. Qualifying coursework may include statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo…