Hybrid Senior Data Scientist – Graph, Identity Analytics

at USAA

• Job title: Senior Data Scientist – Graph, Identity Analytics
• Job type: Full Time
• Experience level: Senior
• Salary: $143,320 - $257,970 per year
• Degree requirement: Bachelor's Degree
• Tech skills: NoSQL, Python, SQL
• Location requirements: Hybrid, San Antonio, United States

About the role

• Develops quantitative solutions addressing identity theft and fraud detection at USAA. Collaborates with teams on machine learning models and graph analytics strategies.

Responsibilities

• The Senior Graph Data Scientist – 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.
• 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

• 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.
• 6 years of experience in predictive analytics or data analysis
• 4 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.
• Proven experience writing code that is easy to follow, well documented, and commented where vital 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.
• Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• 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, and ensemble methods such as Random Forests, XGBoost, LightGBM, and CatBoost.
• 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.

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