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:

Quantitative engineers in Global Risk are responsible for designing and implementing 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 trillions of rows of data) or domain specific (e.g., classification models for surveillance or testing framework for Global Markets processes). Quantitative engineers work with modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. Quantitative engineers have a combination of software engineering, big data, and modeling 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.

Responsibilities:

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.

Skills:

Candidates should meet all or a subset of 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)

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

Qualifications:

At least 2 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

Candidates should have at least one of these following skills and preferably have at least two of these skills:-

Strong analytical and problem-solving 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 

Shift:

Hours Per Week: