Quantitative Engineer
About The Position
This job is responsible for designing, developing, testing and implementing common, reusable, and scalable software components which are either domain independent (generic data quality tools over billions of rows of data) or domain specific (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 modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. Global Risk Analytics (GRA) is a sub-line of business within Global Risk Management (GRM), 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 partners with the Lines of Business and Enterprise functions to ensure the capabilities it builds address both internal and regulatory requirements, and are responsive to the changing nature of portfolios, economic conditions, and emerging risks. In executing its activities, GRA drives innovation, process improvement and automation. 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
- 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)
- 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.
Nice To Haves
- Exposure to quantitative modeling or financial modeling is a plus, but not required.
Responsibilities
- 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
- Collaborates with key stakeholders across the Bank to understand modeling and testing business processes and requirements
- Thinks outside the box of current industry standards to develop innovative approaches
- Maintains and continuously enhances 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
Benefits
- access to paid time off
- resources and support to our employees
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
