Sr. 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!

This job is responsible for designing and overseeing common, reusable, and scalable software components which are either domain independent (e.g., generic data quality tools over billions of rows of data) or domain specific (e.g., 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 senior modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics.

• Seeks opportunities for consistent improvement in quality, efficiency, and processes
• Partners effectively with senior stakeholders within Global Risk Analytics and Enterprise Independent Testing, Front Line Units, Technology, Audit and Compliance
• Provides leadership and oversight to less experienced team members
• 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

Senior Quantitative engineers in Global Risk are responsible for designing and overseeing the implementation of common, reusable, and scalable software components. These components enable GRM’s data and analytical capabilities. These components can be domain independent (e.g., generic data quality tools over billions of rows of data) or domain specific (e.g., classification models for surveillance or testing framework for Global Markets processes).

Senior Quantitative engineers work with senior modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. Senior Quantitative engineers have a combination of software engineering, big data, modeling and management skills and the ability to work across the entire spectrum of a big data stack – from data to logic to model to UI to UX.

• Seek opportunities for consistent improvement in quality, efficiency, and processes
• Partner effectively with senior stakeholders within GRA & EIT, Front Line Units, Technology, Audit and Compliance
• Provide leadership and oversight to less experienced team members
• Applying quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements
• Understanding financial data: schemas, flow, size, data issues, data controls, etc.
• Buildi…