Senior AI Data Scientist Developer

Salary: $143,320 - 273,930 per year Requirements: We require a bachelors degree in mathematics, computer science, statistics, science, engineering, or another quantitative discipline, or 4 years of relevant education and/or experience. We expect 6+ years of experience in predictive analytics or data analysis, or an advanced degree such as a masters or PhD plus 4+ years of experience in predictive analytics or data analysis. We need 4+ years of experience training and validating statistical, physical, machine learning, and other advanced analytics models. We need 4+ years of experience using Python for statistical analysis and/or building and scoring AI/ML models. We look for experience writing clear, well-documented code with comments where needed to explain logic. We require strong experience querying and preparing data from structured and/or unstructured databases using SQL, HQL, NoSQL, or similar languages. We expect demonstrated skill in ad hoc analytics using descriptive, diagnostic, and inferential statistics, along with awareness of real-world constraints such as latency, cost, and reliability in AI solution design. We need the ability to assess and explain regulatory implications and expectations across different modeling efforts, including documentation and statistical validation of models for risk management. We look for advanced experience with classical supervised modeling techniques such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, and forest models. We look for advanced experience with unsupervised modeling techniques such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, and DBSCAN. We require expertise in LLMs and agentic system development using frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or similar, including prompt engineering, tuning and post-training, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring. We need MLOps integration experience supporting engineering implementation of production-scale AI solutions in partnership with AI engineers in cloud environments such as AWS or GCP. We expect experience communicating analytical and modeling results to non-technical business partners with an emphasis on recommendations and actionable outcomes. We expect experience guiding and mentoring junior technical staff in business interactions and model development. Responsibilities: We gather, interpret, and prepare structured and unstructured data to support advanced analytical solutions. We develop scalable, automated solutions using machine learning, simulation, and optimization to generate business insight and value. We choose the most appropriate modeling method or technology based on data constraints, application needs, and business priorities. We develop and deploy models within the Model Development Control and Model Risk Management framework. We prepare technical documentation and support peers in creating documents for knowledge retention, risk management, and technical review audiences. We evaluate business needs and recommend analytical and modeling projects that can create value. We partner with business and analytics leaders to prioritize analytics and modeling problems and research efforts. We build and maintain a robust library of reusable, production-quality algorithms and supporting code. We translate complex business requests into specific analytical questions, perform the analysis or modeling, and communicate results to non-technical colleagues with clear recommendations. We manage project milestones, risks, and obstacles, and escalate issues that may affect success or implementation. We develop best practices for working with Data Engineering and IT to deploy production-ready analytical assets aligned with modeling and risk management standards. We stay current on emerging techniques and actively pursue new methods, technologies, and approaches. We mentor junior data scientists in modeling, analytics, and computer science tasks. We participate in internal communities that support the evolution of data science technologies and culture. We ensure business risks are identified, measured, monitored, and controlled in line with risk and compliance policies and procedures. Technologies: AI AWS Cloud GCP Support Machine Learning MCP MLOps NoSQL Phoenix Python RAG SQL Security Backbone More: At USAA, we are committed to helping our members achieve financial security through competitive products, exceptional service, and trusted guidance, with a strong focus on the military community and their families. We are guided by our values of honesty, integrity, loyalty, and service, and we proudly support active-duty military spouses. This AI Data Solutions Scientist role sits within our Technology organization and Data Science team, working cross-functionally with architecture, engineering, and product partners to advance ne