Intern, Quantitative Risk - Reserve Bank Operations

About the Team & Role

The Quantitative Risk Analysis section is responsible for assessing quantitative risk methodologies developed by the financial sector, or the Reserve Banks, to measure and manage credit, liquidity, counterparty, and market risks, as well as value collateral. The program analyzes risks and contributes to policies and research related to the clearing and settling of payments, securities, financial derivatives, and other financial instruments. The program monitors and evaluates the quantitative risk management of financial market infrastructures (FMIs), financial institutions engaged in systemically important payment, clearing, and settlement (PCS) activities, and the Reserve Banks. The program contributes to quantitative risk research, particularly topics related to FMIs and systemically important PCS activities, and to systemic risk analysis.

Expected Projects:
• Deep Neural Networks vs. Nonlinear econometric models: a project to explore performances of Neural Networks against
a selected set of nonlinear econometric models in practical applications and by using extensive Monte Carlo simulations

Competencies/Learning Objectives:
• Excel in data representation, manipulation, analysis
• Design, train, validate, test and tune Deep Learning and econometric models for empirical analysis to answer questions
in economics and finance

Required Skills and Knowledge:
• BSc or Graduate (Masters or PhD) student in Computer Science, Economics, Business, or any other engineering or science field
• Completed courses on Data Science, Econometrics, and Machine Learning
• Familiarity with Python and/or R
• Interest in economic and financial data and research
• Familiarity with data manipulation, model development, training, validation, and testing approaches
• Interest in economic and financial time series analysis and econometrics

Preferred Skills:
• Experienced in working with time series data and conducting research in economics and econometrics
• Written scripts to design and develop Machine Learning and econometric models
• Completed at least one course on Data Science, Machine Learning, Econometrics, or Deep Learning or relevant classes

Required Documents for application: 
CV and two reference letters.
A copy of an unofficial transcript may be requested.

Suggested Major/Minor: Economics, Finance, Computer Science, Data Science, Machine Learning
Keywords: finance, computer science, data science, statistics

 

Notes:

• US Citizenship is required for all Board internships and applicants must be current students, graduating from their program August 2026 or later. Proof of enrollment will be required.
• This internship must be completed in-person in Washington, DC.
• Candidates must be able to work at a minimum of 20 hours per week during the school year, 40 hours per week during the summer
• This internship duration shall not exceed 180 days from a candidate's start date.