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 is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.
Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

JOB DESCRIPTION:

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.

SKILLS

Candidates should have the following technical skills:

• Software engineering: modular code, software lifecycle processes, unit testing, regression testing
• Big data: distributed computing paradigms (e.g., mapreduce, dataframes, etc), optimizing distributed software
• Modeling / quantitative: basic modeling techniques (regression, classification, clustering, etc)

Soft Skills:

• High level of intellectual curiosity
• Experience implementing strategic process improvements and/or automation
• Proven track record of building relationships and effectively influencing key stakeholders
• Adept at detecting and remediating organizational / process weaknesses and deficiencies
• Feels ownership and accountability for delivering high quality work, able to balance multiple priorities and meet strict deadlines
• Strong written, verbal, presentation creation and delivery skills, typically to very senior audiences
• Well organized with attention to detail
• Willing to challenge the status quo and with a track record of driving change

MINIMUM EDUCATION REQUIREMENT:

• Bachelor’s degree in Computer Science, a closely related field, or a degree from a program where software engineering was a key focus or equivalent work experience
• 7+ years of relevant work experience

QUALIFICATIONS:

• At least 7 years of relevant experience in software engineering in Quantitative Finance or other industries
• Strong Programming skills (e.g., Python) and solid understanding of Software Development Life cycle principles
• Proven track record of seamlessly leading and executing enterprise-wide initiatives
• The candidate must be a strong communicator and able to effectively explain the rationale and details of the methodologies to a broad set of audiences that include key senior stakeholders across the Bank, as well as auditors and regulators
• Strong analytical and problem-solving skills

Candidates should have at least three of the following skills:

• Experience applying quantitative methods such as modelling, data analytics, machine learning, and statistics to develop business solutions
• Experience with large scale data sets with structured or unstructured data
• Experience in building user facing applications over large amounts of data using technologies like React, Angular, JavaScript etc.
• Experience implementing process improvements and automation

• 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.
• Building performant big data pipelines
• Use programming skills and knowledge of software development lifecycle principles to deliver high quality code for model and testing processes
• Collaborate with key stakeholders across the Bank to understand modeling and testing business processes and requirements
• Think outside the box of current industry standards to develop innovative approaches
• Maintaining and continuously enhancing capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks
• Source and evaluate data required for modeling and testing
• Design and develop and implement models and tests
• Produce clear, concise and repeatable technical documentation models and tests for internal and regulatory purposes
  Team Overview:
  Global Risk Analytics (GRA) and Enterprise Independent Testing (EIT) are sub-lines of business within Global Risk Management (GRM). Collectively, they are 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 and EIT partner with all the Lines of Business and Enterprise functions. GRA and EIT include vertical (i.e., business or risk-specific) functions and horizontal functions that cut across business and risk-types. A core pillar of our horizontal strategy is developing common, reusable, and scalable components that can be used across GRM. Quantitative engineers will be responsible for executing on this strategy.