Staff AML Quant Analyst

Netspend; Austin (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

Netspend is seeking a Senior Data Scientist – AML to lead the development and validation of AML monitoring approaches. You will design traditional threshold rules, targeted typologies, and explainable models, coordinating with Compliance and leadership.

Strong SQL/Python skills and the ability to communicate complex analysis to non-technical audiences are essential. The role focuses on large-scale transaction data, label-centric model validation, and governance documentation, with an emphasis on

Qualifications

• Graduate degree (Master’s or Ph.D.) in a quantitative field such as statistics, math, engineering, data science or computer science.
• 6+ years of senior-level data science, quantitative analysis, or statistical modeling experience.
• Strong programming skills in SQL and Python/R; mastery of statistical regression techniques.
• Ability to translate complex math to non-technical stakeholders; excellent written and verbal communication.

Responsibilities

• Build and tune AML monitoring rules using statistical approaches and analyst input.
• Conduct annual AML risk assessments on large transactional datasets.
• Develop explainable statistical models to segment high-risk accounts for targeted review.
• Create and maintain advanced data models and governance artifacts with clear documentation.
• Develop automated dashboards for monthly, quarterly, and annual AML oversight reports.
• Collaborate with Compliance, Fraud, and other departments to implement data-driven solutions.

Skills

SQL

Python

R

Statistics

Data visualization

Communication

Education

Master's or PhD in statistics, math, engineering, data science, or computer science

Tools

Tableau

Qlik

Power BI

Python

R

Job description

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.

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.

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.

Model Building

a. Build effective statistical models using advanced concepts with a focus on effectiveness. This will stand on the foundation of the explainable concepts before it. The focus will be higher likelihood of risky accounts to target for review and due diligence. These are the highly efficient models from above.

Model Documentation & Governance

a. You will be the primary (and sole) person to build professional, master’s level, documentation, to support internal governance, promote understandability, and surpass Model Validation and regulatory benchmarks.

b. Build and maintain automated reporting dashboards (using tools like Tableau, Qlik, Pyton, or R) for Monthly, Quarterly, and Annual Oversight Reporting. You will own the data logic and automation maintenance supporting the reporting and the AML Risk Assessment.

Leadership & Stakeholder Management

a. Cross Functional Collaboration: You will partner with different aspects of Compliance, Fraud, and other business departments to translate business, compliance and regulatory requirements into actionable data driven solutions.

b. Communication: You will translate quantitative findings into actionable strategic recommendations for senior leadership and non-math AML Compliance professionals.

Required Qualifications

1. Education

a. Graduate Degree, Masters or Ph.D., in Statistics, Math, Engineering, Data Science, Computer Science, or high quantitative discipline.

1. Experience

a. 6 years of senior level experience in data science, quantitative analysis, or statistical modeling.

1. Technical skills

a. Professional coding skills, particularly in SQL and Python/R.

b. Masters level statistical regression analysis techniques.

1. Soft skills

a. Translation between business/compliance personal to data personnel.

b. Educating non math/data personnel on Math and Data concepts.

c. Presentation skills to personnel of various backgrounds, math v. non-math, regulatory v. business, etc.

d. Ability to work independently and present potential usable end results to Compliance leadership, while being prepared to return for multiple iterative improvements. Especially for feedback from non-math/data professionals.

e. Ability to list and prioritize known objectives and workstreams. Especially completing smaller one-off tasks, while keeping the bigger, higher overarching goal projects moving timely.

Preferred Qualifications

1. Master’s or Ph.D. in Statistics, Math, or Engineering;

2. 6 years of experience in Risk Management Quantitative Field;

3. Hands on experience with graphical reporting tools (for example, Tableau, Qlik, Power BI);

4. Practical experience with Model Governance and Validation, particularly AML Model Validation and AML Regulatory Exams;

5. Prior experience leading or mentoring teams or individuals.