Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.
Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
We are looking for a highly skilled and analytical Risk Quant to join the Quantitative Strategies & Data Group within Global Markets. The team develops Python-based solutions on the Bank’s strategic platform, Quartz, and provides independent review and challenge of risk and PnL calculations. The team works across all asset classes (Rates / Commodity / Credit / FX / Equity) and collaborates with Quant, Risk and Front Office Technology teams to deliver strategic and regulatory programmes, including FRTB IMA, VaR, Strategic Risk and PnL, etc.. The role offers strong exposure to market risk methodologies, regulatory requirements and data testing frameworks.
Responsibilities:
- Design, develop, implement, and maintain market models (e.g. VaR) to ensure accurate measurement of risk exposures across trading books, in line with regulatory and internal governance requirements
- Support the implementation of risk data testing frameworks to assess the appropriateness, completeness and reasonableness of risk scenarios, VaR, expected shortfall and stress test calculations. This includes testing scenario design, implementation, results consolidation, and analyses of calculations to understand key drivers