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
- Experimented with Generative AI and public data to create chatbot-type applications
- Conducted quality control reviews on products developed by the data science team
Boston:
- Supported the National Stress Testing Program’s initiatives including development, maintenance, and risk management for the supervisory stress test models
- Participated in climate risk-related projects including evaluation of banks’ and mortgages’ exposure to climate risk
- Participated in supervisory exams about currency risk and use of AI/ML models
- Conducted research on risks to financial stability
Chicago:
- Developed and maintained econometric and statistical models used in stress testing to identify and measure risks across various areas such as corporate loans, commercial real estate loans, and asset-backed securities
- Performed a comprehensive risk assessment and evaluated effective management strategies across additional areas such as market risk and model risk management
- Contributed to horizontal examinations and surveillance activities for wholesale credit portfolios, enhancing the oversight and evaluation of credit risks within large financial institutions
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:
- Used advanced statistical approaches to improve early warning models for bank supervision used system-wide
- Worked on stress testing models, monitored banking conditions within the district, and prepared materials and analysis for briefing the Dallas Fed’s president
- Deployed natural language processing and machine learning techniques in new tools for bank supervision
Minneapolis:
- Assisted the System Model Validation group by validating and assessing supervisory models
- Worked with the Stress Testing Program’s Production group on the implementation and production of supervisory stress test models
New York:
- Worked on data visualization and model development projects related to global trading and counterparty credit markets
- Performed quantitative research in areas such as stress testing, impact of financial regulations and development of liquidity risk metrics
- Developed a custom web application to support stress testing operations
- Explored applications of machine learning and natural language processing 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:
- Utilized data analytics, predictive modeling, and visualization to enhance decision-making and operational processes
- Implemented AI, machine learning, and NLP to automate tasks, improve accuracy, and optimize workflows
- Streamlined processes through automation, workflow optimization, and effective resource management, leading to increased productivity and cost-effectiveness