Quantitative Engineer: Global Risk Analytics & Data Pipelines

ghr; Jersey City (NJ); On-site; USD 90,000 - 156,000; Full time

Benefits offered by this job

Discretionary incentive plan

Benefits package

Job summary

Bank of America is seeking quantitative engineers to design, build, test, and implement scalable software components for data quality and analytics across risk management domains. You will work with modelers, risk managers, and technologists to shape the future state of data and analytics.

The role requires software engineering, big data, and modeling skills, with a focus on delivering high-quality code and reusable components within a large data stack.

Qualifications

• Bachelor’s degree in Computer Science or related field or equivalent work experience.
• 1–2 years of professional coding experience or demonstrable personal projects.
• Strong Python development and SDLC knowledge.
• Experience with large-scale data, big data tools, and modeling is a plus.

Responsibilities

• Apply quantitative methods to meet business and regulatory requirements.
• Build performant big data pipelines and data quality tooling.
• Collaborate with modelers, risk managers, and technologists.
• Develop and document models and tests for internal/regulatory use.
• Maintain and evolve data analytics capabilities with changing risks.

Skills

Software engineering

Big data

Modeling

Python

Spark

PySpark

Hadoop

React/JS

Data analytics

Education

Bachelor’s degree in Computer Science or related field

Tools

React

Angular

JavaScript

Spark

PySpark

Hadoop

Hive

Python

MapReduce

Job description

Bank of America is seeking quantitative engineers to design, build, test, and implement scalable software components for data quality and analytics across risk management domains. You will work with modelers, risk managers, and technologists to shape the future state of data and analytics.

The role requires software engineering, big data, and modeling skills, with a focus on delivering high-quality code and reusable components within a large data stack.