Quantitative Engineer

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!

GRA About Us Summary

Global Risk Analytics (GRA) is a sub-line of business within Global Risk Management (GRM). The GRA team provides quantitative capabilities supporting global risk management and capital management and develops a consistent set of risk and capital models, tests and analytical tools that support decision making across the bank.

Job Profile Summary

This job is responsible for building subject matter expertise in bank processes - including underlying data, flows, and controls - and driving the technology evolution in those processes including designing and implementing technical solutions leveraging modular, reusable software components to enhance Global Risk Management’s (GRM) data, testing, and analytical capabilities.

Key responsibilities include collaborating with process owners, risk managers, and technologists to assess current state processes, underlying data, process flows and risks, and to design the future state technology solutions leveraging both traditional and AI based solutions.

Job expectations include becoming domain-specific subject matter expert in bank processes and data, and applying a combination of software engineering, big data, data science and risk management skills to drive technical solution implementation and automate processes.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• Minimum Education Requirements
• 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
• Required Skills
• 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.
• Skills: Critical Thinking
• Data Modeling
• Process Effectiveness
• Risk Modeling
• Test Engineering
• Influence
• Oral Communications
• Prioritization
• Relationship Building
• Written Communications
• Attention to Detail
• Change Management
• Shift: 1st shift (United States of America)
• Hours Per Week: 40