Senior Data Scientist - AML

About the Company:

Netspend Corporation is a global, vertically-integrated financial services and technology company dedicated to the delivery of innovative financial empowerment solutions to consumers worldwide. Netspend's financial products and services span prepaid, debit, cross-border payments, and loyalty solutions for consumers and enterprise partners. Netspend provides prepaid and debit account solutions that connect customers with secure, convenient access to global payment networks so they can manage their money and make everyday purchases.

With a nationwide U.S. retail network, customers can purchase and reload Netspend products at 130,000 reload points and over 100,000 distributing locations. Since our founding in 1999 by industry pioneers, Netspend products have processed billions of dollars in transaction volume and served millions of customers worldwide. The company is headquartered in Austin, Texas with employees worldwide.

The Senior Data Scientist – AML plays a critical role developing and supporting Netspend’s Compliance Department, with a specific focus on AML Compliance. You will be the senior lead, as a team of 1, to develop hybrid, effective, and efficient AML monitoring approaches. The approaches will include, (1) traditional dollar threshold rules based on statistical outlier analysis, (2) targeted rules for high impact scenarios, (3) explainable statistical models, and (4) highly efficient models where effectiveness is the top priority.

You will be

responsible for performing the research, building the rules/models, and creating master’s thesis level documentation intended to explain the work to produce successful AML Model Validation results and regulatory exam results.

This role requires a deep expertise in master's level statistics, an ability to explain the advanced statistics to traditional business and compliance personnel, data programming (SQL and Python preferred), clear plain English writing skills, presentation skills, and the ability to prioritize amongst competing high priorities. This roll will assist with all aspects of Consumer and AML Compliance similar to that of a federally regulated bank, including Anti Money Laundering ("AML"), Customer Identification Program ("CIP"), Consumer Compliance Testing ("CT"), and Distributor Due Diligence ("DDD").

Key Responsibilities

1. AML Rule Tuning & Analytics

a. Build and utilize statistical feedback loops to periodically tune, calibrate, and optimize AML monitoring rules. This is the traditional dollar threshold-based rules referenced above.

b. Build and utilize typologies and AML analyst input to build targeted rules intended for a higher percentage of locating “bad” scenarios. These are the targeted rules referenced above.

2. AML Risk Assessment

a. Perform and update the Annual AML Risk Assessment based on a calendar year of

transactional and account level data (~1 million accounts, ~300+ million transactions).

b. Much of this work is updating existing tables and charts, quality checking the information, updating the word document to ensure the paragraph descriptive statements are correct and publishing this to the Policy Review Committee and Board of Directors for approval.

3. Explainable Statistical Models

a. Build explainable statistical models with a goal of increasing AML effectiveness. The intended purpose is to use the flagged “bad” accounts from the AML Analyst teams and segment accounts into higher risk buckets in order to improve AML analyst time efficiency by targeting riskier accounts. This may cover transaction monitoring, customer risk scoring, OFAC sanctions screening, anomaly detection, with a strict focus on explainability.

These are the explainable statistical models from above.

4. Model Building

a.…