Principal Data Scientist - Remote

At UnitedHealthcare, we're simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together As a Principal Data Scientist within the Optum Technology team supporting UHC Technology, you will lead the design, development, and deployment of advanced machine learning and generative AI solutions. In this role, you will define end-to-end ML architecture, select appropriate tools and frameworks, drive proof-of-concept experiments, and guide engineering teams in productionizing scalable AI services. You will balance architectural leadership with hands-on execution across complex initiatives, establishing best practices for model governance, reproducibility, and security to drive healthcare innovation. You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Design, develop, and deploy scalable, production-grade AI solutions to address complex business challenges while embedding responsible AI principles, fairness, transparency, and accountability throughout the model development lifecycle Lead solution architecture and hands-on development across complex AI/ML initiatives using traditional ML, deep learning, and modern LLM-based approaches Lead proof-of-concept experiments in generative AI (transformers, GANs, diffusion models) and evaluate emerging tools, research, and frameworks to drive strategic innovation Collaborate with research, data engineering, software engineering, and product teams to translate cutting-edge AI advancements into scalable, production-ready capabilities Define and establish enterprise best practices for model governance, versioning, reproducibility, security, and cloud-native MLOps pipelines Provide technical guidance, architectural design reviews, and mentorship to junior engineers and data scientists without formal people management duties Document architecture designs, technical proposals, and recommendations, presenting findings directly to cross-functional stakeholders and leadership You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

10+ years of experience designing, building, and deploying production machine learning solutions 3+ years of experience building Generative AI applications using LLMs and orchestration frameworks such as LangChain and/or LangGraph Deep expertise in either NLP or Computer Vision with multiple years of hands-on solution ownership in that domain Proven solid foundation in core ML and statistical methods (supervised/unsupervised learning, regression, classification, clustering, time series, Bayesian modeling) alongside substantive deep learning experience Demonstrated experience with cloud ML services and infrastructure design on at least one major cloud platform (AWS, Azure, or GCP), including containerization (Docker/Kubernetes) and MLOps (CI/CD, model registry, monitoring) Proven track record of successfully moving models from research/POC phase into production at scale Hands-on programming proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) Demonstrated solid background in probability, linear algebra, and statistical inferenc Demonstrated problem-solving ability with clear verbal and written communication skills Preferred Qualifications: Experience working with healthcare data, clinical systems, or U.S.-based healthcare