Global Quantitative Research Summer Associate Program-2026

Bank of America Corporation • New York, NY

57d

About The Position

Summer associates in our Cross-Asset Quantitative Research program will have the opportunity for up to three rotations during the program within quantitative equity and credit strategy, rates and rate derivatives strategy, equity derivatives research and the quantitative systematic investment strategies team. During the program summer associates will conduct primary research in areas that can include: Applying machine learning techniques to develop strategies for harvesting risk-premia and asset allocation Investigate how markets are influenced by macro and sentiment factors using natural language processing and deep learning Using data science tools for modelling rates curves and positioning, portfolio construction and forecasting market anomalies Identify leading indicators of distress in credit markets and model inflection points in earning with real-time and big data sets Model financial derivatives to identify systematically mispriced assets in order to build alpha generating trading strategies Developing smart dynamic hedging solutions for more efficient risk management of client assets

Requirements

• Candidates are required to be pursuing a Masters or PhD degree from an accredited college or university with a graduation timeframe between November 2026 and August 2027 in a field of Quantitative Finance, Financial Engineering, or a related technical field that blends advanced quantitative methods with finance and economics
• Experience in managing and analyzing large data sets with modern data-science tools is required, strong knowledge and experience in advanced machine learning techniques is critical for select rotations
• Knowledge of financial derivatives including futures and options and their underlying pricing models is key for derivative-team rotations
• Knowledge of Python, Excel and SQL for select rotations
• Distinguished written and verbal communications skills and an ability to communicate complex ideas clearly and concisely to non-technical individuals
• Ability to work independently and drive toward a completed end product
• Strong attention to detail; exercise strong quality control over own work
• A passion and curiosity appropriate for research combined with an ability to rapidly drive results under uncertainty

Responsibilities

• Applying machine learning techniques to develop strategies for harvesting risk-premia and asset allocation
• Investigate how markets are influenced by macro and sentiment factors using natural language processing and deep learning
• Using data science tools for modelling rates curves and positioning, portfolio construction and forecasting market anomalies
• Identify leading indicators of distress in credit markets and model inflection points in earning with real-time and big data sets
• Model financial derivatives to identify systematically mispriced assets in order to build alpha generating trading strategies
• Developing smart dynamic hedging solutions for more efficient risk management of client assets

What This Job Offers

• Career Level: Intern
• Industry: Credit Intermediation and Related Activities
• Number of Employees: 5,001-10,000 employees