Director, Data Scientist

USAA•Charlotte, NC

1d•$189,370 - $361,950

About The Position

USAA is seeking a Director of Data Science to lead the development and scaling of enterprise AI capabilities, including generative AI, large language models (LLMs), and autonomous agent ecosystems. The role combines AI strategy, technical architecture, execution, and organizational leadership, with responsibility for creating reusable AI platforms and solutions that support enterprise-wide automation, innovation, and strategic differentiation. The Director acts as an enterprise AI thought leader and strategic advisor, influencing executives and partnering with business, data, technology, Enterprise Architecture, and IT teams. They lead AI and data science talent in building production-ready LLM applications, retrieval-augmented generation (RAG) pipelines, and autonomous agent systems; guide solutions from ideation through deployment; and establish scalable AI infrastructure and deployment standards. The role also ensures AI solutions meet safety, governance, model risk management, data governance, and regulatory requirements, while managing operational risks such as hallucinations, data leakage, security vulnerabilities, and agent drift. The position includes identifying strategic resource needs and building a high-performing team through recruiting, mentorship, coaching, and performance management. USAA’s stated salary range is $189,370–$361,950, with actual compensation varying by location, experience, and market data. Employees may be eligible for performance-based incentives. USAA does not provide visa sponsorship for this role.

Requirements

At least 8 years of progressive experience in machine learning, advanced data science, and AI development, with recent emphasis on production deployment of generative AI, LLMs, or agentic systems; alternatively, an advanced degree in Computer Science, AI, Data Science, or a quantitative STEM field and at least 6 years of relevant experience.
At least 3 years of direct people leadership experience building, managing, and inspiring technical teams.
Proven track record architecting and delivering scalable, high-performance horizontal AI platforms or reusable capabilities that drive cross-enterprise transformation.
Demonstrated ability to adapt technical strategies and teams in response to rapid changes in the AI ecosystem.
Deep technical background in modern AI frameworks and methods, such as LangChain, LlamaIndex, vector databases, fine-tuning, RAG architectures, prompt engineering, and multi-agent orchestration.
Comprehensive understanding of AI governance, risk mitigation, guardrails, prompt injection defense, alignment, model risk policies, and compliance expectations for automated and autonomous systems.
Exceptional executive communication skills and a proven ability to explain complex AI architectures, ROI metrics, and technical roadmaps to senior executives and non-technical stakeholders.

Responsibilities

Act as an enterprise AI thought leader and strategic advisor, shaping the use of generative AI, LLMs, and agentic workflows.
Influence business, data, and technology executives to invest in and scale enterprise-wide horizontal AI capabilities through strategy, planning, and budgeting.
Direct and empower AI and data science teams to build and deploy scalable LLM applications, RAG pipelines, and autonomous agent systems.
Guide AI solutions from ideation to production, optimizing prompt engineering, context management, model evaluation, and inference cost-efficiency.
Champion modular, reusable horizontal AI capabilities and services for use across business units.
Partner with Enterprise Architecture and IT to establish AI infrastructure, tooling pipelines, and scalable cloud and on-premise deployment standards.
Ensure AI models, agentic workflows, and generative solutions comply with AI safety guidelines, model risk management frameworks, data governance, and regulatory requirements.
Identify and secure strategic resources, including LLM API integrations, third-party tools, specialized hardware, and talent.
Build and scale a high-performing AI team through recruiting, technical mentorship, coaching, and performance management.
Identify, measure, monitor, and control AI operational risks, including hallucinations, data leakage, security vulnerabilities, and autonomous agent drift.