Quantitative Engineer, Risk Analytics & Big Data

Bank of America; Jersey City (NJ); On-site; USD 90,000 - 155,500; Full time

Job summary

Bank of America in Jersey City, NJ is seeking a Quantitative Engineer to design and build scalable data and modeling pipelines within Global Risk. You will collaborate with modelers, risk managers and technologists to turn data into reliable analytics, supporting risk assessment and regulatory reporting.

Strong software engineering and big data experience with Python, Spark and SQL is expected, along with the ability to translate complex financial data into robust, tested code.

Qualifications

• Bachelor’s degree in Computer Science or a related field or equivalent work experience.
• 1–2 years of professional experience or demonstrable coding projects.
• Strong programming skills and familiarity with SDLC.
• Experience with large-scale data sets and data pipelines.
• Proficiency in Python and data analytics libraries.

Responsibilities

• Apply quantitative methods to meet risk management and regulatory requirements.
• Understand financial data schemas, flow and data quality issues.
• Build performant big data pipelines.
• Deliver high quality code for model and testing processes.
• Collaborate with stakeholders across the Bank on modeling and testing needs.
• Develop innovative approaches beyond current industry standards.
• Maintain capabilities to adapt to changing portfolios and risks.
• Source, evaluate, and prepare data for modeling and testing.
• Design, develop, and implement models and tests.
• Produce clear technical documentation for models and tests.

Skills

Strong programming

Python

Analytical thinking

Problem solving

Big data experience

Modeling methods

Software lifecycle

Team collaboration

Education

Bachelor’s degree in CS or closely related field

Tools

Python libraries (Pandas)

Spark

Hadoop

Hive

React/Angular/JavaScript

Job description

Bank of America in Jersey City, NJ is seeking a Quantitative Engineer to design and build scalable data and modeling pipelines within Global Risk. You will collaborate with modelers, risk managers and technologists to turn data into reliable analytics, supporting risk assessment and regulatory reporting.

Strong software engineering and big data experience with Python, Spark and SQL is expected, along with the ability to translate complex financial data into robust, tested code.