Job Description

Bank Of America Quantitative Finance AnalystAt 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!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 driversSupports the planning related to setting quantitative work priorities in line with the bank's overall strategy and prioritizationIdentifies 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/validationSupports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetiteSupports 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 riskWorks closely with model stakeholders and senior management with regard to communication of submission and validation outcomesPerforms statistical analysis on large datasets and interprets results using both qualitative and quantitative approachesOverview of Global Risk Analytics:Bank of America Merrill Lynch has an opportunity for a Quantitative Finance Analyst within our Global Risk Analytics (GRA) function. GRA is a sub-line of business within Global Risk Management (GRM). GRA is responsible for developing a consistent and coherent set of models and analytical tools for effective risk and capital measurement, management and reporting across Bank of America. GRA partners with the Lines of Business and Enterprise functions to ensure that its models and analytics address both internal and regulatory requirements, such as quarterly Enterprise Stress Testing (EST), the annual Comprehensive Capital Analysis and Review (CCAR), and the Current Expected Credit Losses (CECL) accounting standard. GRA models follow an iterative and ongoing development life cycle, as the bank responds to the changing nature of portfolios, economic conditions and emerging risks. In addition to model development, GRA conducts model implementation, data management, model execution and analysis, forecast administration, and model performance monitoring. GRA drives innovation, process improvement and automation across all these activities.The Consumer Model Development & Operations (CMDO) team is part of Global Risk Analytics. It provides quantitative solutions to enable effective risk and capital management across the Retail and Global Wealth & Investments Management (GWIM) lines of business.Overview of the Role:The team places strong emphasis on delivering world class quantitative solutions for Front Line Unit (FLU) model owners and stakeholders through a disciplined and iterative development process. The team has responsibilities across a number of areas: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 presentationsQualified candidates must be able to work independently to provide sound economic reasoning, statistical analysis and deliver high quality modeling insights as well as modeling documentation. The ideal candidate is self-directed, collaborative, analytical, and proactive in execution and problem resolution. Specific tasks include: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.Required Education, Skills & Experience:Successful candidates will have a master's or PhD in Math, Economics, Statistics, or similar discipline, and a minimum of 2 years of relevant experience in statistics, data science, machine learning, model development and other quantitative analysis.Successful candidates will possess the following skills:First-hand experience in data analysis, statistical model estimation, machine learning modeling, implementation, testing, performance evaluation, and model documentationStrong programming skills in Python, SQL, and related quantitative or data science librariesExperience 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 preferredKnowledge of statistical modeling and machine learning techniques, including regression, tree-based methods, model validation, benchmarking, and interpretation of model resultsExperience with HDFS, Hive, Spark, PySpark, and distributed data processing environments for large-scale model development or production analyticsHands-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 methodsExperience 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 understandingAbility to translate quantitative findings into clear business implications for residential property valuation, collateral risk, mortgage or home equity decision support, and model governance stakeholdersQuantitative documentation experience, including clear technical writing and familiarity with LaTeX or similar documentation toolsStrong analytical and problem-solving skills, attention to detail, and ability to work independently while seeking senior modeler and stakeholders' guidanceEffectively presents quantitative analysis, model results, and recommendations to technical and non-technical stakeholdersDesired Skills & Experience: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 processBusiness knowledge of residential real estate, housing markets, appraisal processes, mortgage origination or servicing, home equity lending, collateral risk management, and related economic driversFamiliarity with model risk management, regulatory expectations,

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