Sr. Principal Data Scientist - Remote

UnitedHealth Group•Minnetonka, MN

4d•$134,600 - $230,800•Remote

About The Position

The Data Scientist is a senior-level individual contributor responsible for developing advanced analytics, predictive models, and data-driven insights that directly influence strategic business decisions. This role partners cross-functionally with clinical, product, operations, and client-facing teams to solve complex problems, optimize outcomes, and drive measurable value. The position requires deep expertise in statistical modeling, machine learning, and data engineering principles, along with the ability to translate complex analytics into actionable business strategies. 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

Bachelor's degree in MIS, Statistics, Mathematics, Computer Science
6+ years of experience performing complex data analysis, report development, querying high volume data, and interpreting results
6+ years proficiency in SQL, and data visualization tools (e.g., Tableau, Power BI)
6+ years experience working with large, complex datasets (healthcare data preferred: claims, clinical, or population health datasets)
2+ years of experience in creating relevant databases (tables/fields/libraries) needed to source appropriate data
2+ years of experience building and executing data pulls using SQL and / or other BI reporting tools
1+ years of experience with documenting processes and practices, often in accepted project methodology artifacts

Nice To Haves

Master’s degree in Data Science, Statistics, Computer Science, Mathematics, Economics, or related field
4+ years exposure to cloud-based data platforms (Azure, AWS, GCP)
3+ years experience with real-time or near-time analytics
2+ years experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
2+ years experience in Python
Healthcare industry experience, particularly in population health, care management, product value sales story development and/or payer environments

Responsibilities

Design, develop, and deploy predictive and prescriptive models to support business and clinical initiatives
Apply machine learning, statistical analysis, and optimization techniques to large, complex datasets
Build and validate models for use cases such as risk stratification, utilization forecasting, intervention targeting, and cost containment
Ensure model performance, scalability, and ongoing monitoring
Guide analytics teams in applying machine learning, statistical modeling, and simulation techniques (e.g., regression, classification, clustering, ensemble methods) to large, multi-source datasets
Apply best practices in causal inference, AI/ML, quasi-experimental methods, and forecasting to support strategy decisions and benefit realization
Create and/or complete needed documentation to define project parameters and specifications, process flow and other business documentation including, but not limited to: business/functional/report/analytics requirements, workflows, job aids, and reference materials
Apply analytical, mathematical and statistical techniques to investigating and interpreting data results
Works with less structured, more complex issues, and comfortable with ambiguity
Translate ambiguous business problems into structured analytical frameworks
Deliver actionable insights that inform product development, population health strategies, and client-specific solutions
Support cost-of-care initiatives, cross product benefits, and engagement strategies through analytics
Partner with operations, product managers, consultants, and reporting teams to align analytics with business needs
Communicate findings clearly to both technical and non-technical stakeholders, including executive leadership
Serve as a thought partner in client discussions, RFPs, and strategic initiatives
Partner with data engineering teams to define data requirements, pipelines, and data quality standards
Ensure reproducibility, traceability, and governance of analytical outputs
Work with claims, clinical, and engagement data sources to enable integrated analytics
Identify opportunities to leverage emerging technologies (AI/ML, generative AI, advanced analytics platforms)
Drive enhancements in modeling approaches, tools, and analytical methodologies
Contribute to the development of reusable analytics assets and frameworks
Works on high-priority, enterprise-level initiatives
Influences product strategy, clinical outcomes, and client retention
Supports revenue growth, cost savings, and performance guarantees
Acts as a technical leader and mentor to junior analysts/data scientists (informal leadership)