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.
Primary Responsibilities:
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Portfolio strategy and business outcomes
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Own the strategy, roadmap, prioritization, resource planning, and execution governance for a portfolio of Medicare Risk Adjustment data science, Advanced Analytics and AI initiatives
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Define business problems, solution options, success measures, and accountability for delivery, adoption, quality, productivity, and financial or operational value
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Manage dependencies, milestones, risks, and benefit realization while balancing near-term delivery, platform reuse, technical debt, and long-term capability building
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Technical, AI, and platform leadership
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Provide technical direction across data engineering, Advanced Data Analytics, GenAI, deployment, and monitoring
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Lead architecture, model, and production-readiness reviews; ensure reproducibility, traceability, decision records, and alignment with enterprise standards
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Drive reusable components, reference architectures, MLOps/LLMOps, and evidence-based build-versus-buy or model/vendor decisions
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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
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Responsible AI, compliance, and production excellence
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Ensure compliance with enterprise and healthcare requirements for privacy, security, data use, responsible AI, model governance, and CMS-related processes
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Embed explainability, performance and bias monitoring, human oversight, evidence provenance, validation controls, access controls, and auditable decision trails
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Own lifecycle governance from experimentation through deployment, monitoring, remediation, retraining, versioning, operational handoff, incident review, resilience, and retirement
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Promote disciplined engineering, testing, CI/CD, documentation, observability, and secure development practices
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People and organizational leadership
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Lead, coach, and develop data scientists and analytics professionals; set goals, role expectations, feedback mechanisms, and development plans
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Own workforce planning, hiring, onboarding, performance management, succession, engagement, retention, and allocation of talent to priorities
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Develop technical leaders, raise standards through communities of practice, remove delivery barriers, and foster inclusive accountability and learning
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Executive and cross-functional partnership
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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
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Communicate portfolio status, outcomes, trade-offs, risks, and recommendations; align stakeholders and resolve competing requirements across value, feasibility, compliance, cost, and timelines
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Drive workflow adoption and change management, including clear human-in-the-loop responsibilities and measurement of realized outcomes
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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 regard 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:
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Bachelor's or master's degree in computer science, Engineering, Statistics, Mathematics, Economics, Data Science, or a related quantitative discipline, or equivalent practical experience
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10+ years of relevant Advanced Data analytics, Data science, AI/ML, or advanced analytics experience, including leadership of production-grade solutions and technical teams
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4+ years of solid experience working in Risk Adjustment domain with solid understanding of CMS methodologies, CMS Risk Adjustment financial calculation and working with MMR, MOR, EDPS, encounter, claims, chart, or coding data at scale
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3+ years of solid working experience in designing and developing Risk Adjustment applications through Data Science and Data Analytics initiatives
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Formal people-leadership experience spanning hiring, coaching, performance management, capability building, and delivery through others.
Proven ownership of multiple concurrent initiatives or a solution portfolio with measurable business or operational outcomes -
Advanced proficiency in Python, PySpark, SQL, Spark, Hive, distributed processing, Databricks, and cloud analytics or ML platforms
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Solid expertise in machine learning, statistical modeling, experimentation, optimization, feature engineering, model evaluation and validation, and deep learning frameworks such as PyTorch or TensorFlow
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Working knowledge of LLMs and GenAI, including prompting, retrieval, orchestration, evaluation, guardrails, monitoring, LLMOps, and MLOps/software engineering practices
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Ability to translate ambiguous business and clinical needs into strategies, roadmaps, decisions, and measurable outcomes, supported by solid executive communication and negotiation skills
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Solid programming experience in Cloud technologies
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Solid business domain experience in CMS Risk Adjustment Medicare Line of Business
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Knowledge of Medicare Advantage Risk Adjustment, CMS programs, coding and clinical documentation workflows, or related healthcare analytics
Success measures:
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Portfolio delivery, adoption, business value, and benefit realization against agreed milestones and risk controls
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Model and solution performance, reliability, scalability, cost efficiency, compliance, and audit readiness
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Team capability, leadership pipeline, engagement, retention, platform ownership
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.
