Principal Data Scientist - Remote

Improve the lives of others while Caring. Connecting. Growing together.

Job Description
- Principal Data Scientist
- Remote (2390565)

Principal Data Scientist
- Remote - 2390565

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 product ionizing 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.

Primary 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

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.

Required Qualifications:

• 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 Lang Chain and/or Lang Graph
• 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, Tensor Flow, 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