Quantitative Engineer

Bank of America•Charlotte, NC

12d•Onsite

About The Position

This job is responsible for designing, developing, testing and implementing common, reusable, and scalable software components which are either domain independent (generic data quality tools over billions of rows of data) or domain specific (classification models for surveillance or testing framework for Global Markets processes). Key responsibilities include enabling Global Risk Management's data and analytical capabilities. Job expectations include working with modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. 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. 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.

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
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
Bachelor’s degree in related field or equivalent work experience

Responsibilities

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
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.