Development and implementation of quantitative solutions that improve USAA’s ability to detect and prevent identity theft, account takeover, and first party/synthetic fraud.
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.
Develops and mentors junior staff, establishing a culture of R&D to augment the day-to-day aspects of the job.

Requirements

Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
8 years of experience in a predictive analytics or data analysis
6 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
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.
Expert ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, 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.
Excellent demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Project management experience that demonstrates the ability to anticipate and appropriately manage project landmarks, risks, and impediments.
Demonstrated history of appropriately communicating potential issues that could limit project success or implementation.
Expert level experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic models, discriminant analysis, support vector machines, decision trees, and ensemble methods such as Random Forests, XGBoost, LightGBM, and CatBoost.
Expert level experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building.
Demonstrated ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science.
A strong track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent).
Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.

Benefits

comprehensive medical, dental and vision plans
401(k)
pension
life insurance
parental benefits
adoption assistance
paid time off program with paid holidays plus 16 paid volunteer hours
various wellness programs
career path planning and continuing education

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

predictive analyticsdata analysisstatistical modelingmachine learningPythonRSQLNoSQLgraph databasesgraph neural networks

Soft Skills

project managementcommunicationmentoringcollaborationproblem-solvinginnovationanalytical thinkingteamworkleadershipadaptability