AML ML Modeler Lead - Quant Analytics Manager

U.S. Bank; Chicago (IL); On-site; USD 133,000 - 157,000; Full time

Benefits offered by this job

Healthcare

Retirement plan

Paid vacation

Holiday opportunities

Adoption assistance

Job summary

U.S. Bank is seeking an experienced AML/BI analytics professional to join the Financial Intelligence Unit. You will develop, tune, and monitor AML transaction monitoring models using Actimize SAM, SAS, SQL, and Python, collaborating across EFCC and Model Risk Governance.

The ideal candidate will drive the model development lifecycle, prepare documentation, and communicate complex results to senior leadership and regulators, advancing anomaly detection with true ML approaches.

Qualifications

• Bachelor's degree in a quantitative field with 10+ years of relevant experience
• MA/MS in a quantitative field with 6+ years of related experience
• PhD in a quantitative field with 5+ years of related experience

Responsibilities

• Partner with EFCC and FIU to become familiar with transaction monitoring applications and processes
• Supporting/owning/enhancing existing models
• Driving the full model-development lifecycle from data exploration to production
• Creating robust model development documentation per Model Risk Governance standards
• Ongoing model monitoring in partnership with other FIU teams and performance metrics analysis
• Defending modeling approaches to internal and external examiners

Skills

Regression techniques

Time series

Model validation

Python

SAS

Communication

Education

Bachelor's degree in quantitative field

MA/MS in quantitative field

PhD in quantitative field

Tools

SAS

Python

SQL

PowerPoint

Job description

U.S. Bank is seeking an experienced AML/BI analytics professional to join the Financial Intelligence Unit. You will develop, tune, and monitor AML transaction monitoring models using Actimize SAM, SAS, SQL, and Python, collaborating across EFCC and Model Risk Governance.

The ideal candidate will drive the model development lifecycle, prepare documentation, and communicate complex results to senior leadership and regulators, advancing anomaly detection with true ML approaches.