Data Mining Analyst II
Fannie Mae - Washington, DC

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Fannie Mae exists to expand affordable housing and bring global capital to local communities in order to serve the U.S. housing market. Today, our focus is on preventing foreclosures, making mortgages and rental housing as affordable as possible, and supporting the housing recovery. We are rapidly building and realigning our company to better serve the market as we support the Administration's Homeowner Affordability and Stability Plan.

Join our dedicated, diverse, high-performing workforce and put your unique talents to good use as we work with our partners to advance our nation's housing recovery.

For more information about Fannie Mae and our career opportunities, visit


Use databases and relevant software to plan, design, and execute extractions of data and reports from the database. Work with business unit customer and/or other technical staff to design effective and efficient reports or other analytical use of data. Use data mining technologies and algorithms to search databases in productive ways that will answer client objectives.

  • Confer with business unit managers and staff to determine needs for new or revised analytical needs and suggest new analytical pathways to address those needs.
  • Confer with database administrators, data architects, and data warehouse managers to assess type, scope, and provenance of data available for further or new forms of analysis.
  • With pertinent client and technical staff, develop or consult on specifications of analytical study, new dataset(s), or novel report to be derived from database or data warehouse.
  • Determine formats, structure, and categories of data to be used or extracted in exercise, and relationships between data elements that will produce information of business interest to client.
  • Use or modify variety of data mining algorithms needed to conduct data mining exercise. Document for further development or for execution of exercise.
  • Using algorithms or other mining tools, prepare data for analysis, build pattern libraries, and find relationships or other statistically significant measurements that answer client needs for specified business processes.
  • Consult or provide technical advise on larger project in which data mining methodology may contribute to larger analytical or business intelligence objective(s). Assess feasibility of data mining as component or chosen method of analytical pathway.
  • Bachelor's Degree or equivalent required
  • 4 years of related experience
  • Bachelor's degree in Business, Finance, Economics, Real Estate or equivalent experience required. Advanced degree is strongly preferred.
  • A minimum of five years experience in the real estate or financial services industry required, with an emphasis in the areas of multifamily risk management and/or portfolio analytics.
  • Experience in implementing processes, policies, and methodologies and demonstrated project management skills.
  • Credit risk knowledge and experience, including credit analysis, transaction management, and factors that impact portfolio risk.
  • Quantitative/qualitative financial analysis and methods experience and attention to detail required.
  • Strong interpersonal, communication and relationship management skills with a focus on working effectively in a team environment.
  • Ability to work independently with multiple business partners, on multiple complex projects, have excellent follow-up skills, and meet deadlines and deliverables with minimal to no supervision.
  • Proficiency in automated database, programming languages, spreadsheet, and analysis tools, such as Microsoft Office Suites (Advanced Excel/Access, Word, Advanced PowerPoint) and Advanced SAS, Intermediate SQL, Tableau, Business Objects, etc.


As a condition of employment with Fannie Mae, any successful job applicant will be required to pass a pre-employment drug screen and to successfully complete a background investigation, which may also include a credit check for positions in some areas of our business.

Fannie Mae is an Equal Opportunity Employer.

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