Quantitative Engineer

Bank of America•Charlotte, NC

1d•Onsite

About The Position

The Quantitative Engineer works within Bank of America’s Global Risk Analytics (GRA), a sub-line of Global Risk Management. GRA provides quantitative capabilities for global risk and capital management and develops risk and capital models, tests, and analytical tools to support decision-making across the bank. The role builds subject matter expertise in bank processes, including their data, flows, and controls, and advances the technology used in those processes. It designs and implements technical solutions using modular, reusable software components to improve Global Risk Management’s data, testing, and analytical capabilities. The engineer works with process owners, risk managers, technologists, and other stakeholders to assess current processes and design future-state solutions using traditional and AI-based approaches. The role combines software engineering, big data, data science, and risk management to implement technical solutions and automate processes. Bank of America describes its workplace as committed to an in-office culture that supports collaboration, engagement, and career development, with flexibility based on role responsibilities and business needs. The position is based at 525 Washington Blvd, Jersey City, New Jersey. It is a first-shift role scheduled for 40 hours per week. The annual salary range is $90,000–$155,200, with offers based on experience, education, and skill set. The role is eligible for an annual discretionary award and benefits, including paid time off.

Requirements

Bachelor’s degree or above in a field such as Mathematics, Computer Science, Statistics, Process or Mechanical Engineering, Operations Research, or Data Science, or equivalent work experience.
At least 2 years of relevant software engineering experience in Quantitative Finance or another industry.
Structured analytical thinking, quantitative analysis, and sound judgment to solve complex business, risk, data, and technology problems and drive improvement.
Ability to communicate complex technical concepts to different audiences and build partnerships across stakeholders.
Proficiency in Python and software development best practices, including SDLC, testing frameworks, design patterns, and performance optimization.
Experience developing AI-enabled solutions using modern LLM platforms, agent frameworks, RAG, and MCP patterns.
Strong experience with structured and unstructured data, SQL, APIs, and databases to source, analyze, and integrate data and solve complex analytical problems at scale.
Ability to apply responsible AI practices and evaluate AI solution performance through monitoring, testing, reliability measurement, scalability assessments, and ongoing optimization of cost, quality, and user outcomes.
Knowledge of banking processes, risk management principles, and regulatory requirements.
Skills listed: Critical Thinking, Data Modeling, Process Effectiveness, Risk Modeling, Test Engineering, Influence, Oral Communications, Prioritization, Relationship Building, Written Communications, Attention to Detail, and Change Management.

Responsibilities

Apply critical thinking, sound judgment, and risk management principles to understand business processes, controls, and risks and determine appropriate technical solution designs.
Partner with process owners, data owners, Front Line Units, Technology teams, and other stakeholders to understand requirements, influence outcomes, and communicate technical designs and solution impacts to audiences ranging from practitioners to senior executives.
Use programming expertise, software development lifecycle principles, and AI development patterns to design, build, document, and deploy scalable analytical solutions that support business objectives, regulatory requirements, and operational efficiency.
Source, validate, and analyze data; design scalable data pipelines and analytical solutions across large and complex datasets; and apply quantitative and analytical techniques to identify trends, assess risk, and enhance the firm’s data architecture.
Implement governance, security, observability, and auditability controls for AI-enabled solutions.
Monitor solution performance and improve effectiveness through testing, feedback loops, automation, process optimization, and responsible AI adoption.
Develop subject matter expertise in bank processes and data, and apply software engineering, big data, data science, and risk management skills to implement technical solutions and automate processes.