Principal Data Scientist - Remote

Optum Tech is a global leader in health care innovation. Our teams develop cutting‑edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives.

Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.

We are seeking a seasoned Principal Data Scientist to lead design, development and deployment of advanced machine-learning solutions. In this role you will define end‑to‑end ML architecture, select appropriate tools and frameworks, drive POCs and guide engineering teams in product ionizing scalable AI services. A solid foundation in statistics, deep learning and generative AI, hands‑on cloud expertise, and exceptional communication skills are essential.

The Principal Data Scientist designs and builds production grade ML and GenAI solutions, while providing technical guidance and mentorship to junior engineers without formal people management responsibilities.

You’ll enjoy the flexibility to work remotely
* from anywhere within the U.S., preferably in Minnesota, 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.

• Lead solution architecture and hands on development of machine learning and generative AI applications
• Design, build, and deploy scalable, production grade AI solutions using traditional ML, deep learning, and modern LLM based approaches
• Balance architectural leadership with hands on execution across complex AI/ML initiatives
• Provide technical guidance and mentorship to junior engineers through collaboration and code/design reviews (no people management)
• Partner closely with engineering, product, and cross functional teams to deliver high impact AI solutions aligned with enterprise standards
• Design, develop, and deploy AI‑powered solutions to address complex business challenges
• Lead proof‑of‑concept experiments in generative AI (transformers, GANs, diffusion models) to solve business problems
• Establish best practices for model governance, versioning, reproducibility and security
• Collaborate with data engineers, data scientists, software engineers and product managers to translate business requirements into technical solutions
• Evaluate emerging tools, libraries and research to drive innovation and maintain competitive edge
• Document architecture designs, conduct design reviews and present technical proposals to stakeholders

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
• Deep expertise in either NLP or Computer Vision, with multiple years of hands on solution ownership in that domain
• Deep expertise in core ML and statistical methods: supervised/unsupervised learning, regression, classification, clustering, time series, Bayesian modeling
• Proven solid foundation in traditional ML and deep learning, demonstrated through substantive work prior to or alongside recent GenAI efforts (GenAI only backgrounds without prior ML depth are not sufficient)
• Demonstrated experience with cloud ML services and infrastructure design on at least one major cloud platform (AWS, Azure or GCP)
• Recent experience (approximately last three years) building GenAI applications using LLMs and frameworks such as Lang Chain and/or Lang Graph
• Hands on programming experience in Python and ML frameworks (e.g., PyTorch, Tensor Flow)
• Demonstrated familiarity with big data technologies:
  Apache Spark, Hadoop, Dask
• Ability to define cloud‑native ML infrastructure on Azure, AWS or GCP: containerization (Docker/Kubernetes), ML pipelines (Sage Maker, Vertex AI, Azure ML), MLOps (CI/CD, model registry, monitoring)
• Proficiency…