Quantitative Risk Analyst (Mid-level) - AML Modeling

USAA•Tampa, FL

2d•$93,770 - $179,240•Hybrid

About The Position

We are seeking a Mid-Level Quantitative Risk Analyst with strong expertise in AML (Anti-Money Laundering) modeling and financial crime risk analytics. The ideal candidate will possess at least four years of quantitative risk experience within a large financial institution and a minimum of two years of hands-on AML experience, including transaction monitoring, customer risk scoring, model development, model validation, or financial crime analytics. In this role, you will leverage statistical and quantitative techniques to develop, monitor, and enhance risk models that support AML compliance, risk identification, and regulatory requirements while ensuring model performance and effectiveness. The successful candidate will demonstrate exceptional analytical and problem-solving skills, with a strong attention to detail and the ability to work accurately in a highly regulated environment. They must be an effective communicator who can clearly present complex quantitative findings and risk insights to both technical and non-technical stakeholders. Experience with data analysis tools such as SQL, Python, SAS, or R is preferred, along with a solid understanding of risk management frameworks, model governance, and AML regulatory expectations. The ability to collaborate across compliance, risk, audit, and business teams is essential for success in this role. We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, Phoenix, AZ, Colorado Springs, CO, Charlotte, NC, or Tampa, FL Relocation assistance is not available for this position.

Requirements

Bachelor's degree in Economics, Finance, Statistics, Mathematics, Engineering, Actuarial Sciences, or quantitative field; OR 4 years of relevant education and/or experience.
4 years related quantitative analysis experience in a discipline relevant to risk management to include statistical analysis, modeling, mathematics or other quantitative discipline.
OR advanced degree/designation in Economics, Finance, Statistics, Mathematics, Actuarial Sciences, or other quantitative discipline and 2 years work experience in a quantitative discipline relevant to risk management.
4+ years of quantitative risk, model risk, fraud risk, or analytics experience within a large financial institution.
2+ years of AML-focused experience, including transaction monitoring, customer risk scoring, sanctions, financial crimes analytics, or AML model development/validation.
Knowledge of AML regulations, model governance, and risk management frameworks.
Strong problem-solving abilities with exceptional attention to accuracy and detail.

Nice To Haves

US military experience gained through military service or gained as a military spouse / domestic partner
Bachelor's or Master's degree in Statistics, Mathematics, Economics, Finance, Data Science, or a related quantitative field.
Proficiency in SQL, Python, SAS, R, or similar analytical tools.

Responsibilities

Utilizes advanced analytics to assess future risk, opportunities, and effectiveness and translates results into meaningful solutions to enhance decision making.
Assesses and mitigates exposures through the identification of key and emerging risks in alignment with the risk strategy and appetite.
Applies knowledge to produce analytical material for discussions with cross functional teams to understand complex business objectives and influence solution strategies.
Produces and communicates analytical content for discussions with cross functional teams, governance committees and business process owners to influence business strategies.
May create statistical reporting and financial analysis to forecast results for required stress test scenarios, including analyzing and challenging stress testing results and partnering with relevant functions to validate those results.
May engage in model validations, produce model validation reports, measure and aggregate model risks, and/or report on model issues to committees, auditors, and regulators.