Lead 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.

We are seeking a Lead Data Scientist, Grade 28 to lead the Generative AI, Large Language Model and Agentic AI capabilities for the Vetting Agent initiative. This person will be responsible for designing intelligent AI agents that can reason over reimbursement policies, signal details, claim context, historical decisions and SME feedback to support robust, traceable and explainable payment integrity recommendations.

The role requires a hands-on AI leader who can convert ambiguous business problems into structured AI workflows, guide prompt and agent design, define evaluation frameworks, partner with engineering teams for implementation and ensure outputs are suitable for human review and enterprise governance.

• Primary Responsibilities:

• Generative AI & LLM Leadership

  • Define the LLM strategy for Vetting Agent, including model selection approach, prompt patterns, output structures and governance guardrails
  • Design prompts and structured reasoning flows for policy interpretation, evidence extraction, exception handling and recommendation generation
  • Create reusable prompt templates and evaluation standards that can be consistently applied across agents and use cases
  • Guide safe usage of LLMs for decision-support scenarios, ensuring outputs remain explainable and reviewable by SMEs

• Agentic AI Solution Design

  • Design and lead implementation of agents such as Signal Validation Agent, Policy Evidence Agent, Conflict Detection Agent, Signal Sizing Agent, Recommendation Agent, Explainability Agent and Human Review Support Agent
  • Define how agents collaborate through an orchestration layer, including inputs, outputs, hand-offs, confidence signals and error scenarios
  • Ensure agent outputs are structured, auditable and aligned to downstream workflow needs
  • Partner with engineers to convert agent design into scalable APIs, notebooks, services or orchestration workflows

• Retrieval-Augmented Generation and Knowledge Grounding

  • Design RAG patterns that ground AI responses using reimbursement policies, policy sections, historical edit logic, decision history and validated business knowledge
  • Define retrieval quality expectations, citation requirements, source paragraph mapping and evidence traceability
  • Improve answer consistency by standardising chunking, retrieval, prompt grounding and output schemas
  • Collaborate with data engineering teams to ensure reliable access to curated knowledge and signal context

• AI Evaluation, Quality and Governance

  • Define evaluation frameworks for factual accuracy, completeness, policy coverage, hallucination risk, consistency, explainability and SME review readiness
  • Create test sets and quality gates for agent outputs before moving to business review or production workflows
  • Implement human-in-the-loop controls, audit trails and evidence tracking for all decision-support outputs
  • Partner with Responsible AI and governance stakeholders to document risks, mitigations and review mechanisms

• Technical Leadership and Collaboration

  • Lead and mentor data scientists and AI engineers working on GenAI and agentic AI capabilities
  • Partner with product owners, business analysts, payment integrity SMEs, engineering managers, architects and operations teams
  • Provide technical direction on architecture, implementation patterns, AI quality standards and release readiness
  • Communicate complex AI solution concepts clearly to both technical and business audiences

• 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 arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

• Required Qualifications:

• 8+ years of experience in AI, data science, advanced analytics, AI engineering or related solution delivery
• 3+ years of experience leading AI initiatives, technical teams or complex cross-functional AI delivery
• Hands-on experience building GenAI / LLM-powered enterprise solutions
• Experience designing RAG workflows, prompt frameworks, AI agents or reasoning systems
• Proven solid Python and SQL skills, with working knowledge of enterprise data platforms such as Databricks or equivalent
• Proven solid communication skills with the ability to influence engineering, product, business and governance stakeholders
• Proven ability to translate business requirements into AI workflows, evaluation criteria and implementation-ready specifications

• Preferred Qualifications:

• Healthcare, claims, reimbursement policy, payment integrity or clinical policy experience
• Experience with Azure OpenAI, Azure AI Foundry, Databricks, MLflow or similar enterprise AI platforms
• Experience with LangGraph, Semantic Kernel, LangChain, AutoGen or comparable agent orchestration frameworks
• Experience implementing AI evaluation, prompt governance, model governance, traceability and responsible AI practices
• Experience building solutions that support human-in-the-loop review rather than direct automated business decisions

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

• 8+ years of experience in AI, data science, advanced analytics, AI engineering or related solution delivery
• 3+ years of experience leading AI initiatives, technical teams or complex cross-functional AI delivery
• Hands-on experience building GenAI / LLM-powered enterprise solutions
• Experience designing RAG workflows, prompt frameworks, AI agents or reasoning systems
• Proven solid Python and SQL skills, with working knowledge of enterprise data platforms such as Databricks or equivalent
• Proven solid communication skills with the ability to influence engineering, product, business and governance stakeholders
• Proven ability to translate business requirements into AI workflows, evaluation criteria and implementation-ready specifications
• Healthcare, claims, reimbursement policy, payment integrity or clinical policy experience
• Experience with Azure OpenAI, Azure AI Foundry, Databricks, MLflow or similar enterprise AI platforms
• Experience with LangGraph, Semantic Kernel, LangChain, AutoGen or comparable agent orchestration frameworks
• Experience implementing AI evaluation, prompt governance, model governance, traceability and responsible AI practices
• Experience building solutions that support human-in-the-loop review rather than direct automated business decisions