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Quantitative Finance Analyst at Bank of America – drive risk analytics and develop advanced AI models.
Join a collaborative, inclusive culture and build impactful financial solutions across global markets.
Company and Role Overview
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!
Responsibilities
- 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.
- 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
Global Risk Analytics Overview
Bank of America has an opportunity for a Quantitative Financial Analyst within the Global Risk Analytics (GRA) organization. GRA is part of Global Risk Management (GRM) and is responsible for developing a consistent and coherent set of models and analytical capabilities for effective risk and capital measurement, management, and reporting across the bank. GRA partners with the Lines of Business and Enterprise functions to deliver solutions that address both business and regulatory requirements while remaining responsive to evolving portfolios, economic conditions, and emerging risks. Through its work, GRA drives innovation, process improvement, automation, and analytical excellence.
Consumer Model Development & Operations Overview
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. 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.
- 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.
Qualifications & Skills
Successful candidates will have a Masters or PhD in Computer Science, Engineering, Statistics, or similar discipline, and a minimum 2 year relevant experience.
Successful candidates will possess the following skills:
- Ability to work in a large, complex organization, and influence various stakeholders and partners
- Self-starter; Initiates work independently, before being asked
- Strong team player able to seamlessly transition between contributing individually and collaborating on team projects; Understands that individual actions may require input from manager or peers; Knows when to include others
- Strong communication skills and ability to effectively communicate quantitative topics to technical and non-technical audiences
- Experience with engineering complex, multifaceted processes that span across teams; Able to document process steps, inputs, outputs, requirements, identify gaps and improve workflow
- Strong programming skills, e.g. Python, R, or similar language
- Strong analytical and problem-solving skills
The ideal candidate will possess the following skills and experience:
- Hands-on experience developing and deploying Generative AI applications using large language models (LLMs) and agent-based architectures.
- Knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, prompt engineering, and AI evaluation frameworks.
- Experience building and deploying AI solutions on cloud platforms and integrating models via APIs and enterprise technology platforms.
- Familiarity with LLMOps/MLOps practices, including model monitoring, versioning, governance, testing, and production deployment.
- Experience developing AI-powered productivity tools for documentation automation, workflow orchestration, analytics acceleration, or knowledge management.
- Understanding of responsible AI principles, model risk management, data privacy, explainability, and regulatory considerations within financial services.
- Strong Python development skills with experience leveraging AI/ML frameworks and software engineering best practices, including Git, CI/CD, and automated testing.
Skills:
- 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
Minimum Education Requirement: Master’s degree in related field or equivalent work experience
Shift & Hours
Shift: 1st shift (United States of America)
Hours Per Week: 40