Principal Data Scientist - Remote

UnitedHealth Group•Minnetonka, MN

11h•$112,700 - $193,200•Remote

About The Position

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. Position Summary We are seeking a seasoned Principal Data Scientist to lead the design, development, and deployment of advanced machine learning and generative AI solutions. In this role, you will define end-to-end ML architectures, select appropriate tools and frameworks, drive innovative proof-of-concept experiments, and guide engineering teams in productionizing scalable AI services. With a focus on responsible AI practices, you will design and build production-grade solutions while providing technical guidance and mentorship to junior engineers. A solid foundation in statistical methods, deep learning, generative AI, cloud expertise, and solid communication skills are essential to driving high-impact technology initiatives across the enterprise. 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.

Requirements

10+ years of experience designing, building, and deploying production machine learning solutions
3+ years of recent experience building GenAI applications using LLMs and frameworks such as LangChain and/or LangGraph
Demonstrated experience defining cloud-native ML infrastructure, containerization (Docker/Kubernetes), ML pipelines, and MLOps (CI/CD, model registry, monitoring) on at least one major cloud platform (AWS, Azure, or GCP)
Deep expertise in core ML and statistical methods (supervised/unsupervised learning, regression, classification, clustering, time series, Bayesian modeling) and deep domain expertise in either NLP or Computer Vision with hands-on solution ownership
Solid background in traditional ML and deep learning demonstrated through substantive work prior to or alongside recent GenAI efforts
Hands-on programming proficiency in Python and deep learning frameworks (eg, PyTorch, TensorFlow, Keras)
Solid foundation in probability, linear algebra, and statistical inference with a proven track record of moving models from research/POC into production at scale
Proven problem-solving ability and excellent verbal and written communication skills

Nice To Haves

Experience working with healthcare data, systems, or use cases within US-based healthcare or enterprise environments
Experience with MLOps tools such as MLflow, Kubeflow, TFX, Airflow, or equivalent
Familiarity with big data technologies such as Apache Spark, Hadoop, or Dask
Knowledge of data visualization and dashboarding tools (eg, Tableau, Power BI)

Responsibilities

Lead solution architecture and hands-on development of machine learning and generative AI applications to solve complex business challenges
Design, build, and deploy scalable, production-grade AI solutions using traditional ML, deep learning, and modern LLM-based approaches with an emphasis on responsible AI principles, fairness, transparency, and accountability
Drive proof-of-concept experiments in generative AI (transformers, GANs, diffusion models) and evaluate emerging research, tools, and trends to inform strategic innovation and technical design
Establish best practices for model governance, versioning, reproducibility, security, and integration with enterprise architectural standards
Provide technical guidance, code reviews, and mentorship to junior engineers to foster technical excellence without formal people management responsibilities
Collaborate with data engineers, software engineers, product managers, and cross-functional teams to translate business requirements into re-usable, production-ready capabilities
Document architecture designs, conduct thorough technical design reviews, and present proposals and findings to key stakeholders