Senior AI Data Scientist – Solutions Developer

USAA•San Antonio, TX

7h•Hybrid

About The Position

As a dedicated AI Data Solutions Scientist in the Technology organization at USAA, you will work within our innovative Data Science team and collaborate cross-functionally with our architecture, engineering, and product partners to transform our operations and experiences while producing actionable insights to drive our association forward. As a member of our dynamic community of problem-solvers, you will tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership through scaled solutions and cloud technologies, 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, large/small language models, and advanced agent frameworks. 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 one of the following locations: San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not available for this position.

Requirements

Bachelor’s degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and 6+ years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, ai, or other similar quantitative discipline and 4+ 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 Python for performing statical analysis and/or building and scoring AI/ML models
Experience writing 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, HQL, NoSQL, etc.
Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real-world constraints such as latency, cost, and reliability in AI solution designs.
Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and statistical validation of models for risk management.
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.
Expertise in LLMs and agentic systems development with frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or others, with proven experience including prompt engineering, tuning and post-training techniques, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring.
MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP.
Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.
Experience guiding and mentoring junior technical staff in business interactions and model building.

Nice To Haves

Financial services, insurance, banking, or other highly regulated industry experience.
Experience with cloud-native application development and modernization initiatives.
US military experience through military service or a military spouse/domestic partner

Responsibilities

Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business.
Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences.
Assess business needs to propose/recommend analytical and modeling projects to add business value.
Work with business and analytics leaders to prioritize analytics and modeling problems/research efforts.
Build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
Translate complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.
Manage project milestones, risks, and impediments.
Escalates potential issues that could limit project success or implementation.
Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
Maintain expertise and awareness of cutting-edge techniques.
Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
Serve as a mentor to junior data scientists in modeling, analytics, and computer science tasks.
Participate in internal communities that drive the maintenance and transformation of data science technologies and culture.
Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.