- Company Name
- USAA
- Job Title
- Senior Data Scientist - Fraud Identity Analytics
- Job Description
-
Job title: Senior Data Scientist – Fraud Identity Analytics
Role Summary: Lead the design, development, and deployment of advanced analytical solutions to detect and prevent identity theft, account takeover, and synthetic fraud. Utilize machine learning, graph analytics, and neural network techniques to build models and graph databases that uncover fraud networks and drive risk mitigation decisions.
Expectations: Deliver production‑ready models under the Model Development Control (MDC) and Model Risk Management (MRM) frameworks. Provide continuous innovation in modeling approaches, maintain high-quality source code libraries, produce clear technical documentation, and mentor junior data scientists. Collaborate with strategy, technology, and business stakeholders to prioritize projects and translate analytical insights into actionable business decisions.
Key Responsibilities:
- Build, test, and continuously update identity‑theft, authentication, and fraud detection models.
- Partner with fraud strategy leadership to align model development with business priorities.
- Design and implement a financial‑crimes graph database strategy, including vendor selection and data integration.
- Deploy graph databases and apply graph techniques (e.g., graph neural networks) to identify criminal networks and fraud rings.
- Integrate new data sources to enhance predictive power and improve model performance.
- Export model insights to decision systems for improved fraud targeting.
- Drive innovation in modeling, staying current with emerging techniques and technologies.
- Compose technical documentation for risk management, governance, and knowledge sharing.
- Assess business needs, propose analytical projects, and manage project risks and impediments.
- Develop and maintain reusable, production‑grade algorithms and code repositories.
- Communicate outcomes and recommendations to non‑technical stakeholders.
- Mentor junior scientists and contribute to internal analytics community initiatives.
Required Skills:
- Strong background in machine learning, statistical modeling, and data science.
- Expertise in graph analytics and experience with graph databases (e.g., Neo4j, TigerGraph).
- Proficiency in Python, SQL, and data processing frameworks (Spark, Pandas, etc.).
- Knowledge of model risk management, MDC/MRM frameworks, and model validation standards.
- Ability to translate business problems into analytical questions and actionable insights.
- Excellent written and verbal communication skills; experience documenting technical deliverables.
- Experience with data engineering and deployment pipelines for production models.
- Familiarity with financial crime domains (identity theft, account takeover, synthetic fraud, AML) is highly desirable.
Required Education & Certifications:
- Bachelor’s degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Engineering, or a related quantitative field.
- Minimum of 4 years of relevant data science or analytics experience; advanced degrees (Master’s, PhD) preferred but not mandatory.