Senior Manager 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. The Senior Manager, Data Science (SG29) is a senior people and technical leader accountable for a portfolio of high-impact AI, machine learning, advanced analytics, and Generative AI solutions supporting Medicare Risk Adjustment. The role combines portfolio strategy, technical and platform governance, formal people leadership, and end-to-end accountability for measurable business outcomes. Operating with high autonomy, this leader translates strategy into executable roadmaps, governs key architecture and model-risk decisions, develops data science capability, and partners across product, engineering, clinical, coding, operations, compliance, and executive leadership to deliver scalable, reliable, secure, audit-ready solutions adopted in operational workflows.

Portfolio strategy and business outcomes Own the strategy, roadmap, prioritization, resource planning, and execution governance for a portfolio of Medicare Risk Adjustment data science, Advanced Analytics and AI initiatives Define business problems, solution options, success measures, and accountability for delivery, adoption, quality, productivity, and financial or operational value Manage dependencies, milestones, risks, and benefit realization while balancing near-term delivery, platform reuse, technical debt, and long-term capability building Technical, AI, and platform leadership Provide technical direction across data engineering, Advanced Data Analytics, GenAI, deployment, and monitoring Lead architecture, model, and production-readiness reviews; ensure reproducibility, traceability, decision records, and alignment with enterprise standards Drive reusable components, reference architectures, MLOps/LLMOps, and evidence-based build-versus-buy or model/vendor decisions Guide agentic AI, retrieval, hybrid ML plus rules, and LLM solutions; establish evaluation for grounding, factual consistency, errors, human review, guardrails, monitoring, latency, throughput, and cost Responsible AI, compliance, and production excellence Ensure compliance with enterprise and healthcare requirements for privacy, security, data use, responsible AI, model governance, and CMS-related processes Embed explainability, performance and bias monitoring, human oversight, evidence provenance, validation controls, access controls, and auditable decision trails Own lifecycle governance from experimentation through deployment, monitoring, remediation, retraining, versioning, operational handoff, incident review, resilience, and retirement Promote disciplined engineering, testing, CI/CD, documentation, observability, and secure development practices People and organizational leadership Lead, coach, and develop data scientists and analytics professionals; set goals, role expectations, feedback mechanisms, and development plans Own workforce planning, hiring, onboarding, performance management, succession, engagement, retention, and allocation of talent to priorities Develop technical leaders, raise standards through communities of practice, remove delivery barriers, and foster inclusive accountability and learning Executive and cross-functional partnership Serve as a trusted AI, Data Analytics and Data science advisor to senior business, clinical, coding, product, technology, and operations leaders in Risk Adjustment LOB Communicate portfolio status, outcomes, trade-offs, risks, and recommendations;