Skip to main content

When applying, select your preferred top three home-base locations from among the Federal Reserve Banks. Each of the Banks offers its quantitative fellows unique opportunities.

For example, here’s the type of work previous fellows have done:

Atlanta:

  • Conducted transaction testing models during the examinations of model risk management and business line reviews
  • Developed bank-reporting tools related to risk identification and monitoring
  • Used natural language processing to enhance efficiencies in examinations

Boston:

  • Provided briefings to senior bank management on credit market conditions, trends in banking, and asset markets
  • Conducted research on risks to financial stability
  • Worked on the management, measurement, and understanding of risk associated with supervisory stress test models

Chicago:

  • Worked on a variety of quantitative-related topics within the Wholesale Credit Risk Center
  • Supported the development and maintenance of wholesale credit supervisory stress test models, in terms of both risk identification and measurement
  • Contributed to horizontal examinations and surveillance work for wholesale credit portfolios

Cleveland:

  • Reviewed models and technical aspects of supervisory work, such as model risk management, wholesale and credit models, and market risk
  • Conducted the System’s main horizontal reviews
  • Engaged with select research work, including an artificial intelligence initiative and machine learning projects

Dallas:

  • Developed, maintained, and reviewed supervisory early warning models for bank risk
  • Worked on Systemwide stress testing models, monitored banking conditions in the district, and participated in preparing materials for the briefings of the Dallas Fed’s president
  • Developed a variety of additional tools using machine learning and natural language processing

Minneapolis:

  • Assisted the System Model Validation group by validating supervisory stress test models
  • Worked with the Centralized Production Unit on the implementation and production of supervisory stress test models
  • Assisted on examinations focused on components of preprovision net revenue

New York:

  • Worked on data visualization and model development projects related to global trading and counterparty credit markets
  • Developed a custom web application to support stress testing operations
  • Engaged in the explorations of machine learning and natural language processing applications to supervisory work

Philadelphia:

  • Focused on retail portfolios, including the RADAR group that manages the System’s largest retail data repository
  • Developed the retail supervisory model for DFAST
  • Provided front office and back office support for supervisory activities and bank examinations in the district and conducted supervisory research

Richmond (Charlotte, NC):

  • Contributed to supervisory model development in various risk areas, including operational risk and wholesale credit risk, as well as modeling the effect of the global market shock
  • Supported the LISCC program in multiple risk areas, including operational risk, wholesale credit risk, counterparty credit risk, and market risk
  • Assisted on bank examinations, focusing on model development and model risk management

San Francisco:

  • Participated in the development and production of supervisory stress testing models
  • Worked on data management and analytics, model development and coding, code review, and ongoing model monitoring
  • Developed an expertise in market risk modeling, such as counterparty credit risk, for business-as-usual and stress testing applications
  • Focused on developing supervisory tools and leveraging data science techniques to enhance efficiencies in examinations and develop techniques to quantify nonfinancial risk

St. Louis:

  • Applied emerging technologies, including machine learning and robotic process automation, to improve operational efficiency
  • Conducted research into the future of the community banking business model
  • Explored issues related to household balance sheets and consumer affairs examination data