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.
OptumRx is seeking a highly skilled Senior Data Scientist to lead the development and deployment of advanced AI, Machine Learning, and Generative AI solutions that drive innovation across pharmacy and healthcare operations. This role will leverage large-scale healthcare and enterprise datasets to build predictive models, intelligent applications, and LLM-powered solutions that deliver measurable business and patient outcomes. The ideal candidate will combine deep technical expertise with solid business acumen to solve complex healthcare challenges in a fast-paced, data-driven environment.
Primary Responsibilities
- Generative AI Application Development
- Design, build, and deploy features using Large Language Models (LLMs), including conversational AI, summarization, clinical knowledge extraction, medical document intelligence, and workflow automation
- Implement and optimize Retrieval-Augmented Generation (RAG) pipelines, from data preprocessing and embedding creation to vector search and contextual retrieval
- Apply advanced prompt engineering, fine-tuning techniques (e.g., LoRA), and model adaptation approaches to enhance model performance, accuracy, and domain relevance
- Develop AI-powered solutions leveraging healthcare and clinical datasets, ensuring high accuracy, explainability, and regulatory compliance
- Machine Learning Model Development & Deployment
- Develop and productionize a range of machine learning models, including traditional ML techniques (regression, classification, clustering) and deep learning models
- Perform feature engineering and data preparation on large-scale structured and unstructured datasets, including healthcare, claims, EMR/EHR, provider, member, and operational data
- Build predictive and prescriptive analytics solutions to support healthcare outcomes, risk stratification, utilization management, and operational efficiency
- Establish and manage processes for model evaluation, performance monitoring, drift detection, and continuous retraining
- AI Systems & Integration
- Build and maintain scalable AI services integrating third-party AI APIs (OpenAI, Azure OpenAI, Anthropic, etc.) and open-source models
- Develop agentic AI workflows that orchestrate multiple tools, models, APIs, and healthcare data sources to solve complex business problems
- Work with vector databases (e.g., Pinecone, pgvector, Weaviate) and implement caching strategies to ensure low-latency performance
- Build scalable data pipelines capable of handling billions of healthcare transactions and large volumes of clinical and operational data
- Healthcare Data & Analytics
- Collaborate with healthcare stakeholders to develop AI/ML solutions utilizing EHR/EMR, claims, provider, member, and clinical datasets
- Apply knowledge of healthcare interoperability standards such as FHIR, HL7, ICD, CPT, and SNOMED in data integration and AI solution development
- Ensure compliance with healthcare data privacy, security, and governance requirements
- Collaboration & AI Quality
- Partner with product, business, clinical, data, and engineering teams to translate business needs into deployable AI/ML solutions
- Implement guardrails, evaluation frameworks, and Responsible AI practices to improve reliability and reduce hallucinations
- Participate in code reviews, architecture discussions, and technical mentoring activities.
- Comply with the terms and conditions of the employment contract, company policies and procedures, and all applicable organizational directives
- 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
- Education & Experience:
- B.Tech/M.Tech (Computer Science, AI, Data Science, Robotics) or MCA with 7+ years of professional experience in Data Science and Machine Learning, including 4+ years of hands-on experience building and deploying AI/ML solutions in production environments
- Multiple certifications in Cloud, AI, Machine Learning, or Data Engineering
- 3+ years of experience in Healthcare, HealthTech, Life Sciences, Payer, Provider, Population Health, or Clinical Analytics domains
- Proven experience working with large-scale datasets containing millions to billions of records, including both structured and unstructured data
- Core Programming & Data Skills:
- Solid experience in large-scale data processing, data engineering, and feature engineering
- Experience building scalable data pipelines and distributed data-processing solutions
- Solid proficiency in Python, PySpark, SQL, and ML development frameworks
- Generative AI Expertise:
- Demonstrated experience developing applications using LLMs (GPT, Llama, Claude, Mistral, etc.) and frameworks such as LangChain and LlamaIndex
- Experience leveraging GenAI in healthcare use cases such as clinical summarization, medical document processing, knowledge management, and decision support
- Solid expertise in Prompt Engineering, RAG architectures, vector databases, semantic search, and model fine-tuning
- Machine Learning Foundations:
- Hands-on experience building, training, validating, and deploying ML models
- Solid understanding of supervised learning, unsupervised learning, deep learning, NLP, and statistical modeling
- Experience applying AI/ML techniques to large-scale healthcare, clinical, member, provider, claims, or operational datasets
- Healthcare Data Expertise:
- Understanding of healthcare data models and standards including EHR/EMR, Claims, FHIR, HL7, ICD-10, CPT, and SNOMED
- Experience addressing healthcare data quality, interoperability, governance, privacy, and compliance requirements
- Cloud & Communication:
- Experience with at least one major cloud AI platform (Azure AI, Azure OpenAI, AWS SageMaker, Google Vertex AI)
- Proven excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
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.