Quantitative Engineer

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!

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  Applies quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements

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  Understands financial data: schemas, flow, size, data issues, data controls, etc.

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  Builds performant big data pipelines

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  Uses programming skills and knowledge of software development lifecycle principles to deliver high quality code for model and testing processes

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  Collaborates with key stakeholders across the Bank to understand modeling and testing business processes and requirements

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  Thinks outside the box of current industry standards to develop innovative approaches

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  Maintains and continuously enhances capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks

Global Risk Analytics (GRA) is a sub-line of business within Global Risk Management (GRM), responsible for developing a consistent and coherent set of models, analytical tools, and tests 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 the capabilities it builds address both internal and regulatory requirements, and are responsive to the changing nature of portfolios, economic conditions, and emerging risks. In executing its activities, GRA drives innovation, process improvement and automation. 

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  Analytical Thinking & Risk Management: Apply critical thinking, sound judgment, risk management principles to understand business processes, controls, and risks to determine appropriate designs for technical solutions to business problems.

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  Stakeholder Engagement & Communication: Partner with process owners, data owners, Front Line Units, Technology teams, and other stakeholders to understand business requirements, influence outcomes, communicate technical designs, and impact of solutions to audiences ranging from practitioners to senior executives.

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  Solution Development & Engineering: Leverage programming expertise, software development lifecycle (SDLC) principles, and AI development patterns to design, build, document, and deploy scalable analytical solutions that support business objectives, regulatory requirements, and operational efficiency.

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  Data Engineering & Analytics: Source, validate, and analyze data while designing scalable data pipelines and analytical solutions across large and complex datasets. Apply quantitative and analytical techniques to identify trends, assess risk, and enhance the firm’s data architecture.

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  AI Governance & Operational Excellence: Implement governance, security, observability, and auditability controls for AI-enabled solutions while continuously monitoring performance, driving process optimization, and enhancing solution effectiveness through testing, feedback loops, automation, and responsible AI adoption.

• Bachelor’s degrees or above in fields including but not limited to: Mathematics, Computer Science, Statistics, Process and Mechanical Engineering, Operations Research, Data Science (or equivalent work experience)

• At least 2 years of relevant experience in software engineering in Quantitative Finance or other industries

• Analytical Thinking & Problem Solving: Applies structured thinking, quantitative analysis, and sound judgment to solve complex business, risk, data, and technology challenges while driving continuous improvement and innovation.

• Communication & Stakeholder Management: Effectively communicates complex technical concepts to different audiences and able to build strong partnerships across stakeholders.

• Software Engineering & AI Development: Demonstrates proficiency in Python and software development best practices, including SDLC, testing frameworks, design patterns, performance optimization, and the development of AI-enabled solutions using modern LLM platforms, agent frameworks, RAG, and MCP patterns.

• Data Engineering & Analytics: Strong experience working with structured and unstructured data, SQL, APIs, and databases to source, analyze, and integrate data while solving complex analytical problems at scale.

• AI Governance & Performance Monitoring: Applies responsible AI practices and evaluates AI solution performance through monitoring, testing, reliability measurement, scalability assessments, and continuous optimization of cost, quality, and user outcomes.

• Business & Regulatory Knowledge: Demonstrates knowledge of banking processes, risk management principles, and regulatory requirements, applying business context to deliver effective and compliant solutions.

• Critical Thinking

• Data Modeling

• Process Effectiveness

• Risk Modeling

• Test Engineering

• Influence

• Oral Communications

• Prioritization

• Relationship Building

• Written Communications

• Attention to Detail

• Change Management