Director Data Science

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

• Own the overall AI strategy and technical vision for the team''s data and AI products, aligning capability investments to business value, feasibility, and long-term platform direction
• Drive AI product prioritization and multi-capability roadmaps, and own portfolio-level intake, resource allocation, and cross-BIA prioritization, balancing near-term delivery with a sustainable, scalable architecture
• Hold end-to-end architectural ownership for data and AI products, and be accountable for scalability, accuracy, and governance of models and agentic systems in production
• Innovate AI products that measurably improve productivity and operational efficiency, and systematically identify and remove technical debt to keep the platform maintainable and extensible
• Define enterprise-grade LLM evaluation and quality programs using automated metrics plus structured human review, covering relevance, faithfulness, hallucinations, robustness, bias, readability, and offline/online evaluation strategy
• Own MLOps strategy including model lifecycle management, CI/CD releases, monitoring and drift detection (data/concept), data pipelines/orchestration, architecture/design documentation, and solid Python engineering practices
• Serve as the accountable owner for Responsible AI and enterprise governance across the portfolio, establishing guardrails (prompt-injection defense, toxicity/safety filtering, privacy/PII controls, scope/refusal behavior, adversarial testing, automation-bias mitigation) and ensuring compliance with RAI/AIRB processes, HIPAA, and regulatory requirements in partnership with Legal, Compliance, and Security
• Own the talent engine for the AI/DS team: attract, hire, and retain top talent; build career paths and succession; manage performance; and mentor GL27-GL29 through architecture reviews and technical coaching to raise the overall engineering bar
• Apply solid financial acumen, own business justification and ROI for AI investments, benefit tracking and validation, and cost, and capital tradeoffs that prioritize financial sustainability and value realization
• Set and evangelize a compelling AI vision, and influence senior and executive stakeholders through clear, audience-tailored communication, representing the organization in portfolio reviews, demos, and strategic forums
• Drive AI adoption and culture change across the organization, acting as an AI evangelist, defining and tracking adoption/engagement metrics and feedback loops, and removing barriers to scaled, responsible AI use
• Drive innovation and intellectual property (patents and publications) as a technical thought leader
• Partner with Product, Engineering, and UI/UX leadership to translate ambiguous business needs into differentiated AI capabilities, providing cross-team technical leadership while fostering an inclusive, psychologically safe, high-performing team culture

• A Master''s degree or equivalent in Data Science, Statistics, Mathematics, Computer Science, Machine Learning, Economics, Engineering, or a related quantitative field, or equivalent practical experience
• 3+ years of hands-on experience designing, developing, and validating statistical, machine learning, and/or deep learning models in production or applied research settings, including several years providing technical and team leadership
• 3+ years of experience working with large-scale structured and unstructured datasets, preferably within healthcare, life sciences, financial services, or other regulated domains
• Experience translating ambiguous business or product questions into clearly defined problems, end-to-end solution approaches, and measurable success criteria
• Proven expertise in agentic AI strategy, designing and orchestrating multi-agent, LLM-driven systems, and in bringing them to production reliably
• Deep MLOps expertise across model lifecycle management, CI/CD, monitoring, drift detection, and production governance
• Proven track record establishing AI quality and governance frameworks, including evaluation strategy, Responsible AI guardrails, RAI/AIRB compliance, and audit/traceability for regulated domains
• Demonstrated ownership of AI/ML strategy, technical vision, and system architecture for data and AI products at portfolio or multi-team scale
• Demonstrated solid system architecture skills spanning both data and AI products, with accountability for scalability, accuracy, latency, cost, and security tradeoffs
• Demonstrated people leadership, hiring, developing, and retaining technical talent, managing performance, and building succession and career paths
• Demonstrated solid financial acumen, including business justification, ROI/benefit realization, and cost/capital tradeoff decisions in partnership with Finance
• Demonstrated executive influence and communication skills, able to set and articulate a compelling vision and convey complex technical strategy to both technical and non-technical senior stakeholders

• Experience with deployment and orchestration platforms and managing scalable ML/LLM workloads in production
• Experience designing cloud-native AI architectures on Azure, AWS, and/or Google Cloud that balance latency, reliability, and security
• Experience with MLOps tooling for experiment tracking, pipeline automation, model registry, and governance
• Experience implementing robust evaluation and observability for GenAI systems beyond development environments, including drift detection for prompts/responses and operational alerting for quality regressions
• Experience in healthcare, life sciences, or other regulated domains, including value-based care, risk models, and disease progression use cases
• Track record of innovation such as filed patents, published work, or novel approaches that advanced AI capability and productivity, and experience representing the organization at external industry forums