Principal Data Scientist - Remote

UnitedHealth Group•Minnetonka, MN

2d•$112,700 - $193,200•Remote

About The Position

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.

Requirements

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 inference
Demonstrated problem-solving ability with clear verbal and written communication skills

Nice To Haves

Experience working with healthcare data, clinical systems, or U.S.-based healthcare environments
Practical experience fine-tuning generative models (e.g., GPT, BERT, Stable Diffusion, or custom architectures)
Experience with MLOps tools such as MLflow, Kubeflow, TFX, or Airflow
Familiarity with big data technologies including Apache Spark, Hadoop, or Dask
Knowledge of data visualization tools (Tableau, Power BI) and dashboard design

Responsibilities

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