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Quantitative Analytics Professional

Freddie Mac76 open roles

Where
McLean, VA
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Your applicationOpen nowQuantitative Analytics ProfessionalFreddie Mac · McLean, VA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Freddie Mac's own form.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. Freddie Mac postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted today

Freddie Mac median: 1 days open

The posting

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

Freddie Mac’s Single-Family Modeling team is seeking a Quantitative Analytics Professional to develop and enhance collateral models using state-of-the-art statistical methods, machine learning, and data technologies. Your work will inform daily decisions about mortgage collateral and support effective risk management across Freddie Mac’s portfolio.

You will collaborate with modeling teams and business partners to research, prototype, develop, and validate models using large-scale structured and unstructured data. You will apply distributed computing, advanced machine learning, and emerging AI—including GenAI where appropriate—to improve collateral models and modernize modeling workflows, with a focus on business value, reproducibility, and strong governance.

Our Impact

Freddie Mac is leading the U.S. housing market forward by making homeownership and rental housing more accessible and affordable. Our team develops robust, scalable models that support the Single-Family business by enabling critical, data-driven risk decisions.

Your Impact

  • Collateral Modeling and Research: Develop and validate analytical methods and models to assess collateral risk for new and existing financial and mortgage products. Research enhancements to existing models, conduct experiments, develop prototypes, and apply industry best practices to address business needs.
  • Advanced Technology Application: Evaluate and apply emerging statistical, machine learning, and AI technologies—including GenAI where appropriate—to improve collateral models and modeling workflows, consistent with business objectives, regulatory requirements, and enterprise risk management frameworks.
  • Model Development Practices: Apply effective tools and practices for model development, version control, and reproducibility. Improve consistency, transparency, and quality across modeling teams.
  • Data and Computing: Use distributed computing and data-processing technologies to analyze large-scale structured and unstructured datasets and modernize end-to-end model pipelines.
  • Independent Problem-Solving and Business Support: Independently develop practical solutions to complex modeling problems under limited direction. Provide analytical support and day-to-day technical expertise to business and product partners.
  • Collaboration and Communication: Collaborate across teams to translate business needs into analytical solutions. Document and communicate research findings, model results, and recommendations to technical and business audiences.

Qualifications

  • Master's degree in quantitative finance, statistics or a related quantitative field.
  • Coursework and / or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets.
  • Qualifying coursework may include—but is not limited to—statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods/modeling.
  • Coursework or experience applying statistical, machine learning, or econometric methods to large datasets.
  • Proficiency in Python, SQL, and Spark for predictive modeling and processing large-scale structured and unstructured data; experience with relational or NoSQL databases.
  • Familiarity with LLMs and GenAI applications, including prompt design, evaluation, or agentic workflows, is a plus.
  • Experience with end-to-end model development; financial services or regulated-industry experience is a plus.
  • Strong problem-solving skills and ability to manage competing priorities in a fast-paced environment.
  • Excellent written and verbal communication skills for technical and business audiences.

Keys to Success in this Role

  • Technical Contribution: Develop and enhance collateral models and analytical solutions that address business needs and deliver measurable value.
  • Quantitative Research and Data Skills: Apply strong quantitative, empirical analysis, and research skills, supported by hands-on experience processing large volumes of structured and unstructured data.
  • Innovation Mindset: Demonstrate curiosity, initiative, and independent thinking. Learn and apply emerging statistical, machine learning, AI, and data technologies to improve models and development workflows.
  • Cross-Functional Collaboration: Build effective working relationships across teams and contribute constructively to technical discussions and decisions.
  • Clear Communication: Explain complex concepts concisely, communicate research findings effectively, and present a clear project narrative for technical and business audiences.

Current Freddie Mac employees please apply through the internal career site.

We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under applicable federal, state or local laws. We will ensure that individuals are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

A safe and secure environment is critical to Freddie Mac’s business. This includes employee commitment to our acceptable use policy, applying a vigilance-first approach to work, supporting regulatory mandates, and using best practices to protect Freddie Mac from potential threats and risk. Employees exercise this responsibility by executing against policies and procedures and adhering to privacy & security obligations as required via training programs.

CA Applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Notice to External Search Firms: Freddie Mac partners with BountyJobs for contingency search business through outside firms. Resumes received outside the BountyJobs system will be considered unsolicited and Freddie Mac will not be obligated to pay a placement fee. If interested in learning more, please visit www.BountyJobs.com and register with our referral code: MAC.

Time-type:Full time

FLSA Status:Non-Exempt

Freddie Mac offers a comprehensive total rewards package to include competitive compensation and market-leading benefit programs. Information on these benefit programs is available on our Careers site.

This position has an annualized market-based salary range of $105,000 - $157,000 and is eligible to participate in the annual incentive program. The final salary offered will generally fall within this range and is dependent on various factors including but not limited to the responsibilities of the position, experience, skill set, internal pay equity and other relevant qualifications of the applicant.

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