Senior Data Scientist – Fraud Identity Analytics

About Usaa

Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.

Job Summary

The Senior Data Scientist – Fraud Identity Analytics is responsible for development and implementation of quantitative solutions that improve USAA’s ability to detect and prevent identity theft, account takeover, and first party/synthetic fraud. These solutions will range from the development of machine learning models to broad implementation of solutions such as graph analytics to protect USAA and our Members from risks emanating from these threats.

Key Responsibilities

• Develop and continuously update internal identity theft and authentication models to mitigate fraud losses and negative member experience from fraud application, synthetic fraud and account takeover attempts.
• Closely partner with Strategies team, Director of Fraud Identity Analytics and Director of Fraud Model Management and Model Users on model builds and priorities.
• Partner with Technology and other key collaborators to deploy a Financial Crimes graph database strategy, including vendor selection, business requirements, data needs, and clear use cases spanning financial crimes.
• Deploy graph databases and graph techniques to identify criminal networks engaging in fraud, scams, disputes/claims and AML and deliver highly significant benefits.
• Generate and prioritize fraud-dense rings to mitigate losses and improve Member experience.
• Identify and work with technology to integrate new data sources for models and graphs to augment predictive power and improve business performance.
• Exports insights to decision systems to enable better fraud targeting and model development efforts.
• Drives continuous innovation in modeling efforts including advanced techniques like graph neural networks.
• Collaborate with the broader analytics community to share standard methodologies and techniques.

Requirements

• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develops scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations.