Mid-Level AML Quant Risk Analyst - Data-Driven & Remote

usaa; United States; Hybrid; USD 94,000 - 179,000; Full time

Job summary

USAA seeks a Mid-Level Quantitative Risk Analyst with strong AML modeling and financial crime analytics expertise. The role requires at least four years of quantitative risk experience in a large financial institution and a minimum of two years in AML, including monitoring, scoring, or model development.

The ideal candidate will leverage SQL, Python, SAS, or R to develop and validate risk models, translate findings for stakeholders, and collaborate across compliance, risk, audit, and business

Qualifications

• Four years of quantitative risk experience in a large financial institution.
• Two years of hands-on AML experience including monitoring, risk scoring or model development.
• Strong communication of complex quantitative findings to stakeholders.

Responsibilities

• Utilizes advanced analytics to assess future risk and translates results into solutions.
• Identifies key and emerging risks and aligns with risk strategy and appetite.
• Prepares analytical material for cross-functional discussions and governance.
• Produces analytical content for governance committees and process owners.
• May forecast stress test results and validate findings with relevant functions.
• May engage in model validations and report model issues to committees/regulators.

Skills

SQL

Python

SAS

R

Education

Bachelor's degree in Economics, Finance, Statistics, Mathematics, Engineering, Actuarial Sciences, or quantitative field

Advanced degree in Economics, Finance, Statistics, Mathematics, Actuarial Sciences, or related quantitative discipline

Job description

USAA seeks a Mid-Level Quantitative Risk Analyst with strong AML modeling and financial crime analytics expertise. The role requires at least four years of quantitative risk experience in a large financial institution and a minimum of two years in AML, including monitoring, scoring, or model development.

The ideal candidate will leverage SQL, Python, SAS, or R to develop and validate risk models, translate findings for stakeholders, and collaborate across compliance, risk, audit, and business