Principal Data Scientist - Remote

UnitedHealth Group•Eden Prairie, MN

13h•$112,700 - $193,200•Remote

About The Position

The Clinical Decision Support (CDS) Engineering team, a unit within the Optum Insight Technology organization, is responsible for building commercial products that help payers and providers with administrative- and clinician-focused CDS solutions. In this role as a Principal Data Scientist, you will lead high-impact AI initiatives characterized by scale and complexity. You will architect robust, secure AI systems, train state-of-the-art transformer and multimodal models, and bridge the gap between AI research and production software engineering. By extracting deep insights from healthcare data and maintaining the highest standards of responsible and ethical AI, you will help shape the next generation of intelligent health solutions. 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 industry or academic experience in machine learning or a related data science field
5+ years of experience building machine learning models for business applications
5+ years of experience leading technical teams or complex AI/ML projects
4+ years of equivalent technical experience in software development/data science
3+ years of experience with Python programming and deep learning frameworks (e.g., PyTorch or TensorFlow)
3+ years of experience with state-of-the-art ML architectures (e.g., Transformers/LLMs, multimodal modeling) and transformer fine-tuning

Nice To Haves

Experience working with complex healthcare datasets and clinical/claims analytics
Practical experience with public cloud AI technology stacks, deployment, and MLOps methodologies (AWS preferred)
Experience with big data processing tools such as Hadoop and Spark
Experience with state-of-the-art ML algorithms and architectures (e.g., Transformers/LLMs, state space models such as Mamba, GNNs), legacy architecture maintenance/migration, and tradeoffs between pretraining from scratch vs. adapting foundation models

Responsibilities

Drive end-to-end generative AI and machine learning projects that have a high degree of ambiguity, scale, and complexity
Design robust AI architectures, ensuring that systems are scalable, secure, and efficient; select appropriate technologies, frameworks, and methodologies
Build machine learning models, perform proof-of-concept experiments, optimize and deploy models to production, and partner with software engineers to productionize ML models
Perform hands-on analysis and modeling of healthcare data sets to develop insights that increase business value
Run A/B experiments, gather data, and perform statistical analysis; continuously assess AI system performance and optimize algorithms and models to improve accuracy and efficiency
Establish scalable, efficient, automated processes for large-scale data analysis, machine learning model development and validation, and MLOps
Research and implement innovative machine learning approaches, evaluate emerging trends to inform solution design, and translate cutting-edge AI advancements into production-ready capabilities that can be re-used across the enterprise
Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle
Mentor and help recruit AI/ML scientists and machine learning engineers to the team