Quant jobs / Bank of America

Quantitative Engineer – Global Risk Analytics

An engineering role in Bank of America's Global Risk Analytics group, building reusable software and data tooling for risk models and tests. It increasingly involves AI-based tools such as LLM agents.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Learn the bank's risk processes and data flows, then automate them in code
Build data pipelines and analytical tools for risk testing
Prototype AI-assisted workflows with governance and monitoring controls
Work with risk managers and technologists to document and deploy solutions

What they ask for

Required

Bachelor's or above in a quantitative or technical field (or equivalent experience)
2+ years of software engineering
Python and software development lifecycle practice
SQL, APIs and databases
LLM, agent and retrieval pattern knowledge
Stakeholder communication

Education: Bachelor's or above in mathematics, computer science, statistics, engineering, operations research or data science, or equivalent work experience
Experience: At least 2 years of software engineering in quantitative finance or other industries
Languages and tools: Python
SQL

How to prepare

Build a tested Python data pipeline with quality checks and logging
Learn how bank risk data flows from trading systems into risk reports
Prototype a small retrieval-augmented tool and measure its accuracy
Revise testing frameworks and design patterns

What to study

The Risk Quant roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Bank of America’s site before applying: requirements, location and pay can change after we check them.

View the posting on Bank of America’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Bank of America and doesn’t handle applications.