Data Scientist - Gen AI

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.

The Opportunity

We are the core Data Science team for the Enterprise Data Science, Strategy and Analytics organization at USAA. As a member of our dynamic community of innovators, you will tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership, leveraging both structured and unstructured data through traditional pillars of operations research such as simulation, optimization, and machine-learning techniques, as well as a heavy emphasis on cutting-edge technologies with generative AI and large language models. This team is the backbone of the next generation of AI modeling at USAA, and we hope you join us on the frontier!

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 the following locations: San Antonio, TX, Plano TX or Phoenix, AZ. Relocation assistance is not available for this position.

What you'll do:

• Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes technical documents for knowledge persistence, risk management, and technical review audiences. Consults with peers for guidance, as needed.
• Translates business request(s) into specific analytical questions, executing on the analysis and/or modeling, and communicating outcomes to non-technical business colleagues.
• Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions that are aligned with the customer’s vision and specifications and consistent with modeling best practices and model risk management standards.
• Seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

What you have:

• Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; 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.
• 2 years of experience in predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline
• Experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
• 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.
• Ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
• 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.
• Familiarity with performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• 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.
• Experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
• Ability to communicate analytical and modeling results to non-technical business partners.

What sets you apart:

• Experience with Large Language Models or Generat