Senior Applied Data Scientist - AI/ML

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:

• Translate business problems into AI/ML, Generative AI, and Agentic AI solution approaches

• Conduct hands-on experimentation using machine learning, Generative AI, Agentic AI, and emerging AI technologies

• Design, build, and validate proof-of-concepts (POCs) and prototypes to assess technical feasibility, business value, scalability, and operational readiness

• Develop production-oriented POCs that establish implementation patterns, reusable assets, architecture guidance, deployment approaches, and operational considerations required for enterprise adoption

• Create reusable prompts, workflows, evaluation frameworks, reference architectures, solution accelerators, and implementation assets for broader organizational adoption

• Drive successful transition of validated POCs into production by partnering closely with engineering teams to ensure solutions are scalable, maintainable, secure, and aligned with enterprise architecture standards

• Develop implementation-ready artifacts including reusable code components, prompt libraries, workflow templates, deployment recommendations, evaluation methodologies, and technical documentation to accelerate engineering adoption

• Own the technical readiness of AI solutions by proactively identifying scalability constraints, operational dependencies, implementation risks, and mitigation strategies during experimentation

• Apply AI Development Lifecycle (AIDLC) practices during experimentation phases, including:

• Structured evaluation and benchmarking

• Iterative model refinement

• Experiment tracking and documentation

• Performance and cost optimization

• Document learnings, experimentation results, architectural recommendations, and reusable solution assets

• Develop Generative AI solutions leveraging:

• Retrieval-Augmented Generation (RAG) architectures

• Prompt engineering and optimization techniques

• Vector databases and semantic retrieval frameworks