Lead Graph Data Scientist - Identity Analytics

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.

We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs.

The Opportunity

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, Chesapeake, VA or Tampa, FL.

Relocation assistance is not available for this position.

Job Description

The Lead Graph Data Scientist - Identity Analytics is responsible for development and implementing quantitative solutions that improve USAA's ability to detect and prevent identity theft, account takeover, and first-party/synthetic fraud. These solutions range from machine learning model development to enterprise deployment of graph analytics capabilities that protect USAA and our Members from these threats. Strong candidates will be able to deliver the following work products and processes:

• Develop and continuously update internal identity theft and authentication models to mitigate fraud losses and reduce negative member experience from fraud applications, synthetic fraud, and account takeover attempts
• Closely partner with the Strategy team, Director of Fraud Identity Analytics, Director of Fraud Model Management, and model users on model builds and priorities.
• Partner with Technology and other key collaborators to deploy a Member Protection graph technology 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, improving fraud detection and loss mitigation
• 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
• Develops and mentors junior staff, establishing a culture of R&D to augment the day-to-day aspects of the job

What you'll do:

• Gathers, interprets, and manipulates sophisticated structured and unstructured data to enable sophisticated analytical solutions for the business.
• Leads and conducts sophisticated analytics demonstrating machine learning, simulation, and optimization to deliver business insights and achieve business objectives.
• Guides the team selecting the appropriate modeling technique and/or technology with consideration for data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes and peer reviews technical documents for knowledge persistence, risk management, and technical review audiences.
• Partners with business leaders from across the organization to proactively identify business needs and propose/recommend analytical and modeling projects to generate business value.
• Works with business and analytics leaders to prioritize analytics and highly sophisticated modeling problems/research initiatives.
• Leads efforts to build and maintain a robust library of reusable, production-quality algorithms and supporting code to ensure model development and research efforts are transparent and based on highest-quality data.
• Assists the team with translating business request(s) into specific analytical questions, implementing analysis and/or modeling, and communicating outcomes to non-technical business colleagues with a focus on business action and recommendations.
• Manages project portfolio milestones, risks, and impediments. Anticipates potential issues that could limit project success or implementation and escalates as needed.
• Establishes and maintains standard methodologies for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk managem