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 Analytics and AI Enablement will lead an enterprise analytics portfolio spanning business intelligence, reporting, advanced analytics, and insight delivery while helping the organization responsibly adopt AI/ML-enabled capabilities. This role will translate complex business needs into scalable analytics solutions, lead multidisciplinary teams, modernize data and reporting practices, and partner with business, product, data science, engineering, architecture, and governance teams to deliver measurable outcomes.
The position requires solid analytics leadership and practical exposure to predictive analytics, machine learning, Generative AI, natural language processing, and intelligent automation. It is designed for a leader who can identify high-value use cases, define business and data requirements, guide evaluation and adoption, and partner effectively with technical specialists; it is not a hands-on AI platform engineering role.
Primary Responsibilities:
•Analytics strategy and portfolio leadership: Define and execute a multi-year analytics roadmap aligned with business priorities; manage dependencies, risks, and measurable value across reporting, BI, advanced analytics, and AI-enabled initiatives
•Insight delivery: Lead the development, enhancement, validation, and operation of dashboards, scorecards, self-service analytics, predictive insights, and decision-support solutions that are accurate, timely, scalable, and actionable
•Business partnership: Work with senior business, product, operations, technology, and data leaders to translate strategic questions into prioritized analytics use cases, clear measures of success, and sustainable operating solutions
•People leadership: Lead, coach, and develop managers, analytics leads, data analysts, BI professionals, and business analysts; manage capacity, performance, succession, skills development, and delivery commitments
•Analytics modernization: Advance modern BI, cloud data, reusable metric, semantic model, automation, and self-service practices; reduce fragmented reporting and manual effort in partnership with data engineering and architecture teams
•Applied AI/ML enablement: Identify opportunities where predictive analytics, machine learning, Generative AI, NLP, or intelligent automation can improve decisions, experiences, productivity, or operational outcomes
•AI/ML use-case definition: Convert business challenges into well-defined AI/ML use cases with expected value, data needs, evaluation criteria, human oversight, implementation dependencies, and adoption plans
•Cross-functional delivery: Partner with data scientists, AI/ML engineers, architects, product managers, data engineers, and governance teams to develop, validate, operationalize, and monitor AI-enabled analytics solutions
•Evaluation and responsible adoption: Establish business acceptance criteria and support evaluation of analytical and AI outputs for accuracy, relevance, consistency, explainability, traceability, fairness, and operational usefulness
•Governance and controls: Establish standards for data quality, metric definitions, lineage, access, privacy, security, regulatory compliance, responsible AI, and analytics lifecycle management
•Operational excellence: Monitor service levels, delivery performance, solution adoption, model or insight effectiveness, incidents, risks, and realized business benefits; drive continuous improvement and timely issue resolution
•Executive communication: Present complex analytical findings, options, risks, and recommendations in clear, decision-oriented language for senior stakeholders
•Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements
Required Qualifications:
•Bachelor's degree in Data Science, Analytics, Computer Science, Statistics, Information Systems, Engineering, Business Analytics, or a related field, or equivalent professional experience
•12+ years of experience in data, analytics, business intelligence, reporting, technology, or related functions
•5+ years of experience leading analytics, BI, reporting, or data teams, including responsibility for performance, capability development, resource planning, and delivery outcomes
•Demonstrated experience managing an enterprise analytics portfolio and influencing senior business and technology stakeholders
•Solid experience translating complex business needs into analytical solutions, roadmaps, requirements, and measurable outcomes
•Experience with SQL, data analysis, dimensional or semantic data concepts, data visualization, and enterprise reporting
•Experience with BI platforms such as Power BI, Tableau, Cognos, or equivalent technologies
•Experience working with cloud data and analytics platforms such as Snowflake, Databricks, Azure, AWS, or equivalent technologies
•Experience establishing or operating analytics governance, metric governance, data-quality controls, delivery standards, or service-level processes
•Practical exposure to predictive analytics, machine learning, Generative AI, NLP, or AI-enabled business solutions, including defining use cases and partnering with data science or engineering teams
•Demonstrated ability to assess analytical or AI-enabled outputs using business, quality, risk, and adoption criteria
•Proven solid executive communication, strategic planning, people leadership, change management, and cross-functional delivery skills
