Quantitative Engineer - Consumer & Wholesale

Bank of America•Jersey City, NJ

2d•$90,000 - $155,500•Onsite

About The Position

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! 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.

Requirements

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
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.
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)

Nice To Haves

Exposure to quantitative modeling or financial modeling is a plus, but not required.

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