Quantitative Finance Analyst

Bank of America•Charlotte, NC

About The Position

The Quantitative Finance Analyst conducts quantitative analytics and modeling projects for specific business units or risk types. The role develops models, analytical processes, and systems approaches; creates technical documentation; and works with Technology staff on systems for running models. It requires broad knowledge of financial markets and products. Within Bank of America’s Global Risk Analytics organization, the AMG Team develops and manages models across Wholesale, Consumer, and Global Financial Crimes, with potential work in areas such as Finance, Markets, Capital, and Physical Climate Risk. The analyst supports enhancements to existing Wholesale and Consumer loss forecasting models, develops new models, and contributes throughout the model development lifecycle, including regulatory-driven alternative and champion models. The analyst proactively identifies suitable statistical estimation techniques, gathers business requirements from stakeholders, develops modeling solutions, implements models using governed Python code, and produces technical documentation for internal and regulatory use. The role also promotes GRA best practices for model development, implementation, and monitoring, and calls for innovative approaches to modeling problems.

Requirements

Master’s degree in a related field or equivalent work experience; a highly numerical degree is required, with a PhD desirable.
Some experience developing, documenting, and maintaining risk and/or capital models and handling large datasets.
Knowledge of statistics, probability theory, econometrics, and financial mathematics.
Strong programming skills in SQL, Python, VBA, and LaTeX.
Strong technical writing and clear verbal communication skills.
Ability to work under pressure and deliver to tight deadlines.
Ability to work independently, multitask, and prioritize work appropriately.
Curiosity and willingness to develop and work on new modeling approaches.
Ability to think innovatively about modeling problems beyond current industry standards.

Nice To Haves

PhD-level education.
Experience in financial risk modeling, loss forecasting, or related areas.
Knowledge of regulatory guidelines, including CCAR, DFAST, CECL, and ICAAP.
Strong stakeholder engagement skills and ability to work with colleagues in business, risk, and model validation functions.
Organization, practicality, execution focus, and some project management experience.
Self-motivation and intellectual curiosity about the role, supporting technologies, and the wider bank.
Experience with LaTeX.

Responsibilities

Conduct end-to-end market risk stress testing, including scenario design and implementation, results consolidation, internal and external reporting, and analysis of scenario drivers.
Support planning and prioritization of quantitative work in line with the bank’s overall strategy.
Identify continuous improvements by reviewing model development or validation approval decisions, providing feedback on technical documentation, and effectively challenging development and validation work.
Support model development and model risk management in assigned areas to meet business requirements and the enterprise’s risk appetite.
Provide methodological, analytical, and technical guidance to challenge and influence development and validation projects and identify potential risks.
Communicate submission and validation outcomes to model stakeholders and senior management.
Perform statistical analysis on large datasets and interpret results using qualitative and quantitative approaches.
Seek effective statistical estimation techniques for modeling risks across supported businesses.
Work with business stakeholders to gather requirements and develop modeling solutions.
Implement models using well-written, governed Python code.
Prepare clear technical documentation for internal and regulatory purposes.
Promote and follow GRA best practices for model development, implementation, and monitoring.
Contribute across the model development lifecycle and to regulatory-driven alternative and champion models.