Quantitative Engineer

Bank of America • Jersey City, NJ

2d • $90,000 - $155,200 • Onsite

About The Position

The Quantitative Engineer builds subject matter expertise in bank processes, including underlying data, flows, and controls, and drives technology evolution by designing and implementing technical solutions with modular, reusable software components. The role enhances Global Risk Management’s data, testing, and analytical capabilities.

The position partners with process owners, risk managers, technologists, and other stakeholders to assess current processes, data, flows, and risks, then design future-state solutions using traditional and AI-based approaches. It combines software engineering, big data, data science, and risk management skills to implement technical solutions and automate processes. Global Risk Analytics, the team this role supports, provides quantitative capabilities for global risk and capital management and develops risk and capital models, tests, and analytical tools for decision-making across the bank.

The role is based at 525 Washington Blvd, Jersey City, New Jersey, and follows a first shift schedule of 40 hours per week. Bank of America describes its work culture as in-office, with flexibility based on role responsibilities and business needs. The stated annualized pay range is $90,000 to $155,200, with offers determined by experience, education, and skills. The role is eligible for an annual discretionary incentive plan and employee benefits, including paid time off.

Requirements

• Bachelor’s degree or above in Mathematics, Computer Science, Statistics, Process or Mechanical Engineering, Operations Research, Data Science, or a related field; equivalent work experience is also accepted.
• At least 2 years of relevant software engineering experience in Quantitative Finance or another industry.
• Structured thinking, quantitative analysis, and sound judgment to solve complex business, risk, data, and technology challenges and drive continuous improvement.
• Ability to communicate complex technical concepts to different audiences and build strong stakeholder partnerships.
• 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 working with structured and unstructured data, SQL, APIs, and databases to source, analyze, and integrate data and solve complex analytical problems at scale.
• Knowledge of responsible AI practices and evaluating AI solution performance through monitoring, testing, reliability measurement, scalability assessments, and continuous optimization of cost, quality, and user outcomes.
• Knowledge of banking processes, risk management principles, and regulatory requirements.
• Skills listed include critical thinking, data modeling, process effectiveness, risk modeling, test engineering, influence, oral and written communications, prioritization, relationship building, 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 suitable 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 their impacts to audiences from practitioners to senior executives.
• Design, build, document, and deploy scalable analytical solutions using programming expertise, software development lifecycle principles, and AI development patterns to 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 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.

Benefits

• Paid time off
• Industry-leading benefits and resources and support for employees
• Eligibility for an annual discretionary incentive award based on individual performance and behaviors, line-of-business or group performance and contributions, and overall company success

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level