Quantitative Finance Analyst at Bank of America

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 and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates' physical, emotional, and financial wellness through affordable, competitive and flexible benefits. We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve. Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

Job Description:
This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products.

Responsibilities:

• Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers
• Supports the planning related to setting quantitative work priorities in line with the bank's overall strategy and prioritization
• Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation
• Supports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite
• Supports the methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk
• Works closely with model stakeholders and senior management with regard to communication of submission and validation outcomes
• Performs statistical analysis on large datasets and interprets results using both qualitative and quantitative approaches

• Quantitative Modeling - Develop and maintain risk and capital Models and Model Systems across Retail and GWIM product lines. Models and Model Systems provide insight into many risk areas, including loan default, exposure at default (EAD), loss given default (LGD), delinquency, prepayment, balances, pricing, risk appetite, revenues and cash flows, automated valuation model (AVM) for residential properties.
• Quantitative Development - Architect, implement, maintain, improve and integrate quantitative solutions on strategic GRA platforms. Outputs include GRA libraries that perform consumer risk model calculations, analytical tools, processes and documentation. Partner in defining, adopting, and executing GRA's technical strategy.
• Risk and Capital Management Capabilities - Build best in class quantitative solutions that enable the Retail and GWIM lines of business to effectively manage risk and capital, through the application of the disciplined BAU development process that includes extensive interaction with the FLU model owners and stakeholders throughout the quantitative lifecycle.
• Infrastructure - Partner in driving forward the infrastructure to support the goals of GRA through code efficiencies, and expansion of quantitative capabilities to better leverage infrastructure and computational resources.
• Documentation - Deliver concise, quantitative documentation to inform stakeholders, meet policy requirements, and enable successful engagement in regulatory exams (e.g., CCAR, CECL) via automated, modularized, and standardized documentation and presentations

• Set priorities related to quantitative modeling in line with the bank's overall strategy and prioritization.
• Work with senior modelers to develop and design best in class models to satisfy stakeholder requirements.
• Identifies continuous improvement through reviews and ongoing monitoring of models, and effective challenges on model development and validation.
• Work closely with Technology Team to support model execution.
• Collaboration with Enterprise Model Risk Management to support model validations, and quickly and efficiently resolve outstanding issues.
• Support business units and act as a subject matter expert on specified quantitative modeling techniques, as well as oversee model performance, model risk and model governance on critical model portfolios.
• Work closely with senior modelers and stakeholders with regards to communication of submission and validation outcomes.

• First-hand experience in data analysis, statistical model estimation, machine learning modeling, implementation, testing, performance evaluation, and model documentation
• Strong programming skills in Python, SQL, and related quantitative or data science libraries
• Experience working with large and complex datasets, including data extraction, transformation, validation, feature engineering, and quality review using SQL-based tools; exposure to Hive, Spark, or PySpark is preferred
• Knowledge of statistical modeling and machine learning techniques, including regression, tree-based methods, model validation, benchmarking, and interpretation of model results
• Experience with HDFS, Hive, Spark, PySpark, and distributed data processing environments for large-scale model development or production analytics
• Hands-on experience with machine learning or AI model development using Python-based frameworks such as scikit-learn, boosting (e.g., XGBoost and LightGBM), bagging (e.g., Random Forest), or related ensemble methods
• Experience with model explainability and transparency techniques, such as SHAP, feature importance, partial dependence, interpretable models, or other methods used to support model governance and stakeholder understanding
• Ability to translate quantitative findings into clear business implications for residential property valuation, collateral risk, mortgage or home equity decision support, and model governance stakeholders
• Quantitative documentation experience, including clear technical writing and familiarity with LaTeX or similar documentation tools
• Strong analytical and problem-solving skills, attention to detail, and ability to work independently while seeking senior modeler and stakeholders' guidance
• Effectively presents quantitative analysis, model results, and recommendations to technical and non-technical stakeholders

• Ability to apply CI/CD and software engineering tools, such as Git, JIRA, Confluence, Pytest, Jenkins, SonarQube, and code review practices, in a controlled model development process
• Business knowledge of residential real estate, housing markets, appraisal processes, mortgage origination or servicing, home equity lending, collateral risk management, and related economic drivers
• Familiarity with model risk management, regulatory expectations, and governance processes for high-impact financial models, including documentation, validation, monitoring, change management, and audit readiness
• Experience with CCAR, CECL, consumer credit risk, or other regulated banking model development environments

• Critical Thinking
• Quantitative Development
• Risk Analytics
• Risk Modeling
• Technical Documentation
• Adaptability
• Collaboration
• Problem Solving
• Risk Management
• Test Engineering
• Data Modeling
• Data and Trend Analysis
• Process Performance Measurement
• Research
• Written Communications

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