Quantitative Engineer Analyst

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!

• →

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

• →

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

• →

  Builds performant big data pipelines

• →

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

• →

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

• →

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

• →

  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. 

• →

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

• →

  Stakeholder Engagement & Communication: Partner with process owners, data owners, Front Line Units, Technology teams, and other stakeholders to understand business requirements and build understanding of the firms processes and technology landscape.

• →

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

• →

  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.

• →

  AI Governance & Operational Excellence: Understand governance, security, observability, and auditability control requirements for AI-enabled solutions including continuous monitoring of performance. Develop understanding on how to drive process optimization, and enhance 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)

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

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

• Software Engineering & AI Development: Demonstrated strong programming skills (e.g., Python), analytical and problem-solving, digital fluency, and exposure to AI technologies, including large language models (LLMs), agentic AI concepts, or AI-assisted development tools through coursework, internships, personal projects, or professional experience.

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

• Analytical Thinking

• Critical Thinking

• Oral Communications

• Prioritization

• Written Communications

• Attention to Detail

• Change Management

• Collaboration

• Presentation Skills