Campus - Data Science Intern (Analytics & Modeling Program)

Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose-driven innovation that expands access to homeownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home.

Job Description

This 10-week internship program, ( June 7, 2027-August 13, 2027), offers a set hourly rate of $41.50 an hour and a potential pathway to full-time opportunities with Fannie Mae .

This position has multiple openings and will be based in our Washington, D.C. (Midtown Center) office.

WHY STUDENTS CHOOSE FANNIE MAE

• Gain hands-on experience as a real contributor working on projects and business challenges that help shape the future of housing finance.
• Build your career at a company that combines enterprise-scale impact with a mission-driven culture focused on creating opportunities for people and communities nationwide.
• Work in a technology-enabled environment that encourages curiosity, innovation, and the responsible use of emerging tools, including artificial intelligence.
• Collaborate with experienced professionals and cross-functional teams solving complex business and technology challenges.
• Benefit from professional development opportunities, mentorship, networking events, and exposure to leaders across the company.
• Develop in-demand skills through meaningful work, training, and real-world experience in a collaborative and supportive environment.
• Make an impact from day one while helping support one of the most important sectors of the U.S. economy.
• Access benefits available to program participants, including Fannie Mae’s commuter benefit and 401(k) plan through Fidelity.

THE IMPACT YOU WILL MAKE

As a Data Science Intern , you will develop and apply technical and analytical skills by researching open-ended questions, leveraging our data to produce insights, and providing consultative services to extend Fannie Mae's dynamic credit risk management capabilities.

You will participate in an initial training period covering both business knowledge and technical skills. The business courses provide exposure to key business areas at the company and the role of analytics in supporting them. The technical courses include SQL, Python, and R, as well as an introduction to working with technology and data platforms that include Redshift, Domino Data Lab, and Git/Bitbucket.

After training, you will join a team where you will work on one project for the remainder of the program. This project will culminate in a presentation to the division where you will practice and demonstrate effective communication through data visualizations and supporting analytical narrative. Throughout the summer, you will also participate in hackathons in areas such as Geospatial Analysis and Logistic Regression alongside your fellow Analytics Interns to gain broader exposure to the Data Science world.

Examples of intern projects include:

• Researching production model improvements through back-testing, text analysis, and data mining
• Investigating techniques to identify anomalous data across metrics and within varying loan populations
• Developing a prototype model to identify risk indicators for property quality

THE EXPERIENCE YOU BRING TO THE TEAM

Minimum Qualifications

• Authorization to work in the U.S. without sponsorship
• Academic achievement (preferred GPA of 3.2 or higher)
• Shows curiosity and adaptability in learning and responsibly applying new technologies, including artificial intelligence, to reimagine how we work.

Preferred Qualifications

• Currently pursuing a Bachelor's or Master's degree with an expected graduation date of Spring 2028
• Mathematics, Statistics, Computer Science, Systems Engineering, Economics with a quantitative focus, and Data Science majors
• Exposure to programming languages such as R, Python, and SQL
• Open and honest communicator with demonstrated leadership capabilities
• Quantitatively minded with an aptitude for analytical storytelling
• Fluent with technology applications and excited to learn new technologies
• Demonstrated problem-solving with a solutions-oriented approach
• A self-starter who’s comfortable asking questions and persevering through unforeseen challenges

Enterprise - Analytics and Modeling Program - Intern

Target Pay Range: $41.50 an hour

#LI-DNI

Qualifications

Education:

The future is what you make it to be. Discover compelling opportunities at Fanniemae.com/careers.

For most roles, employees are expected to work onsite on a regular basis at their designated office location. In-office work cadence is determined by your manager. Proximity within a reasonable commute to your designated office location is preferred unless the job is noted as open to remote.

Fannie Mae is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, sex, national origin, disability, age, sexual orientation, gender identity/gender expression, marital or parental status, or any other protected factor. Fannie Mae is committed to providing reasonable accommodations to qualified individuals with disabilities who are employees or applicants for employment, unless to do so would cause undue hardship to the company. If you need assistance using our online system and/or you need a reasonable accommodation related to the hiring/application process, please complete this form.

The hiring range for this role is set forth below. Final salaries will generally vary within that range based on factors that include but are not limited to, skill set, depth of experience, certifications, and other relevant qualifications. This position is eligible to participate in a Fannie Mae incentive program (subject to the terms of the program). As part of our comprehensive benefits package, Fannie Mae offers a broad range of Health, Life, Voluntary Lifestyle, and other benefits and perks that enhance an employee's physical, mental, emotional, and financial well-being. See more here.

Requisition compensation:

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