Principal Data Scientist

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.

Provide technical leadership for a major area of the AI and Data Science portfolio, translating the broader AI strategy and product vision into executable architecture, capability roadmaps, and measurable outcomes Lead prioritization and technical sequencing of AI products and capabilities within the assigned domain, balancing business value, feasibility, delivery risk, reuse, and long-term platform direction Hold end-to-end architectural ownership for complex data science and AI capabilities, with accountability for scalability, accuracy, latency, cost, reliability, security, and maintainability in production Lead the design and production delivery of agentic and LLM-driven systems, including retrieval, tool use, orchestration, state management, evaluation gates, human-in-the-loop controls, and robust failure handling Define and implement 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 Establish Responsible AI guardrails for owned capabilities, including prompt-injection defense, toxicity and safety filtering, privacy/PII controls, scope and refusal behavior, adversarial testing, automation-bias mitigation, auditability, and alignment with RAI/AIRB processes Lead end-to-end MLOps and LLMOps design, including reproducible experimentation, model and prompt lifecycle management, CI/CD, release controls, monitoring, drift detection, observability, rollback, data pipelines, orchestration, and technical documentation Innovate AI products and reusable technical patterns that measurably improve productivity, decision support, and operational efficiency across multiple use cases Identify and remove technical debt across data, modeling, evaluation, and deployment workflows, improving extensibility, reuse, engineering quality, and speed of delivery Serve as the senior technical authority for complex modeling and architecture decisions, facilitating design reviews, resolving cross-team technical dependencies, and setting high standards for Python, SQL, testing, documentation, and peer validation Mentor GL26-GL28 data scientists and support the technical growth of GL29 peers through hands-on coaching, architecture reviews, code and model reviews, reusable guidance, and communities of practice Partner with Product, Engineering, Methods, UI/UX, Security, Legal, and Compliance to translate ambiguous business needs into differentiated AI capabilities with clear success criteria, governance requirements, and production operating models Communicate complex technical strategy, architecture decisions, risks, and results to technical and non-technical senior stakeholders through clear documentation, presentations, demos, and recommendations Drive technical innovation through prototypes, reusable assets, intellectual property, publications, or novel approaches that advance AI capability and create sustainable business value Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work