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.

Primary Responsibilities

• 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 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
  
  

• 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, validating, and deploying statistical, machine learning, deep learning, and/or AI solutions in production or applied research settings
• 3+ years of experience working with large-scale structured and unstructured datasets, preferably within healthcare, life sciences, financial services, or other regulated domains
• Experience implementing Responsible AI controls and governance for regulated environments, including privacy/PII safeguards, safety guardrails, auditability, documentation, and review-board compliance
• Experience establishing AI evaluation and quality frameworks using automated metrics, structured human review, error analysis, adversarial testing, and offline/online measurement
• Deep proficiency in Python, SQL, distributed processing frameworks such as Spark or equivalent technologies, and software engineering practices for production-quality AI systems
• Advanced expertise in agentic AI and LLM systems, including RAG, prompt design, tool/function calling, structured outputs, multi-step orchestration, human-in-the-loop patterns, and production reliability
• Demonstrated expertise in MLOps and LLMOps, including lifecycle management, CI/CD, monitoring, observability, drift detection, pipeline orchestration, model registry workflows, and production governance
• Demonstrated success serving as a lead individual contributor or technical lead for complex, cross-functional data science or AI initiatives without relying on formal people-management authority
• Proven ability to translate AI strategy and ambiguous product needs into technical architecture, sequenced roadmaps, end-to-end solution approaches, and measurable success criteria
• Proven solid system architecture skills spanning data and AI products, with experience making tradeoffs across scalability, accuracy, latency, cost, reliability, privacy, security, and maintainability
• Proven solid communication and stakeholder-influence skills, with the ability to explain complex technical decisions, risks, and recommendations to both technical and non-technical senior audiences
• Demonstrated ability to mentor data scientists, raise technical standards through architecture and code reviews, and influence technical direction across multiple teams
  
  

• Experience with deployment and orchestration platforms for scalable ML/LLM workloads, including containerization and Kubernetes or equivalent technologies
• Experience designing cloud-native AI architectures on Azure, AWS, and/or Google Cloud that balance latency, reliability, cost, security, and operational supportability
• Experience with MLOps tooling for experiment tracking, pipeline automation, model registry, evaluation, observability, and governance
• Experience implementing robust post-deployment monitoring for GenAI systems, including prompt and response drift, quality regression detection, operational alerting, and feedback loops
• Experience in healthcare, life sciences, or other regulated domains, including value-based care, risk models, clinical or operational workflows, and complex multi-source data
• Experience facilitating technical communities of practice, developing standards and reference architectures, or representing an organization in internal or external technical forums
• Track record of technical innovation through patents, publications, reusable frameworks, open-source contributions, or novel approaches that advanced AI capability or productivity