Senior Data Scientist

📋 Description

• • As a Senior Data Scientist specializing in Identity Analytics at USAA, you will be at the forefront of protecting our members and the organization from sophisticated threats like identity theft, account takeover, and synthetic fraud.
• • This pivotal role involves the end-to-end development and implementation of advanced quantitative solutions, leveraging cutting-edge machine learning and graph analytics techniques.
• • You will be instrumental in building and continuously refining internal models designed to detect and mitigate fraud, thereby minimizing financial losses and enhancing the member experience by preventing negative impacts from fraudulent activities.
• • A key aspect of this position is close collaboration with cross-functional teams, including the Strategies team, Director of Fraud Identity Analytics, and Director of Fraud Model Management and Model Users, to align on model development priorities and strategic direction.
• • You will play a crucial role in partnering with Technology and other stakeholders to define and execute a comprehensive Financial Crimes graph database strategy, encompassing vendor selection, defining business requirements, identifying data needs, and establishing clear use cases across various financial crime domains.
• • The implementation of graph databases and advanced graph techniques will be essential for identifying complex criminal networks involved in fraud, scams, disputes/claims, and Anti-Money Laundering (AML) activities, delivering substantial benefits to the organization.
• • You will be responsible for generating and prioritizing fraud-dense rings, a critical step in effectively mitigating losses and improving the overall member experience.
• • Identifying and integrating new, innovative data sources will be a continuous effort to augment the predictive power of our models and graphs, leading to improved business performance.
• • Insights derived from your analysis will be exported to decision systems, enabling more precise fraud targeting and informing future model development efforts.
• • You will drive continuous innovation within our modeling efforts, actively exploring and implementing advanced techniques such as Graph Neural Networks (GNNs) to stay ahead of evolving threats.
• • Collaboration with the broader analytics community is encouraged to share best practices, standard methodologies, and innovative techniques, fostering a culture of shared learning and advancement.
• • Your responsibilities will extend to gathering, interpreting, and manipulating both structured and unstructured data to build sophisticated analytical solutions that address complex business challenges.
• • You will develop scalable, automated solutions using machine learning, simulation, and optimization techniques to deliver actionable business insights and tangible value.
• • Selecting the most appropriate modeling technique and technology, considering data limitations, application context, and specific business needs, will be a core competency.
• • You will be responsible for developing and deploying models in strict adherence to the Model Development Control (MDC) and Model Risk Management (MRM) frameworks, ensuring compliance and robustness.
• • Composing and assisting peers with the creation of technical documentation is vital for knowledge persistence, risk management, and technical review processes.
• • Proactively assessing business needs and recommending analytical and modeling projects that can drive significant business value will be a key contribution.
• • You will build and maintain a robust library of reusable, production-quality algorithms and supporting code, ensuring transparency and high-quality data for all model development and research efforts.
• • Translating complex business requests into specific analytical questions, executing the analysis or modeling, and effectively communicating outcomes to non-technical business colleagues, with a strong focus on actionable recommendations, is paramount.
• • You will actively manage project breakthroughs, risks, and impediments, escalating potential issues that could hinder project success or implementation.
• • Developing standard methodologies for engaging with Data Engineering and IT teams is crucial for deploying production-ready analytical assets that align with best practices and model risk management standards.
• • Maintaining expertise and staying abreast of innovative techniques and technologies is expected, with active pursuit of learning opportunities.
• • You will serve as a mentor to junior data scientists, guiding them in modeling, analytics, and computer science tasks, fostering their professional growth.
• • Participation in internal communities dedicated to the maintenance and transformation of data science technologies and culture will be encouraged.
• • Ensuring that risks associated with business activities are optimally identified, measured, supervised, and controlled in accordance with established risk and compliance policies and procedures is a fundamental responsibility.