Quantitative Engineer - Consumer & Wholesale

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!

• →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

• 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
• A minimum of 1-2 years relevant professional experience or evidence of personal projects and endeavours that show a passion for coding and problem solving. 
• 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
• Strong Python development skills (including Pandas and related data-processing libraries).
• Experience with big data technologies such as Spark, PySpark, Hadoop, and Hive.
• Exposure to quantitative modeling or financial modeling is a plus, but not required.
• 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)

• Critical Thinking
• Data Modeling
• Process Effectiveness
• Risk Modeling
• Test Engineering
• Influence
• Oral Communications
• Prioritization
• Relationship Building
• Written Communications
• Attention to Detail
• Change Management