USAA is looking to empower its members to achieve financial security by detecting and preventing identity theft, account takeover, and first party/synthetic fraud through the development and implementation of quantitative solutions.
Requirements
- 4 years of experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models
- Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
- Strong experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
- Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
- Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
- Experience guiding and mentoring junior technical staff in business interactions and model building
- Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results
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
- 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
Other
- Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field
- 6 years of experience in a predictive analytics or data analysis
- 4 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models
- Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts
- US military experience through military service or a military spouse/domestic partner
