Chief Data Scientist – UnitedHealthcare

UnitedHealth Group•Minnetonka, MN

4h•Hybrid

About The Position

At UnitedHealthcare, we’re simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together. The UnitedHealthcare Chief Data Scientist is a strategic leader responsible for establishing a new data science capability with the remit to transform data, advance analytics and AI into measurable enterprise value across the healthcare insurance ecosystem. This role ensures that advanced predictive analytics directly supports core business objectives, including affordability, quality of care, member experience, financial and actuarial planning and operational efficiencies. The Chief Data Scientist operates at a senior level and partners with senior UnitedHealthcare business leaders to identify high-impact transformative opportunities and deliver solutions that drive impact. The senior leader will work cross functionally with the UnitedHealth Group and Optum teams to secure efficient, scalable and sustained solution design and deployment. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. Hybrid in MN/DC: This position follows a hybrid schedule with four in-office days per week.

Requirements

15+ years of proven executive leadership experience in advanced analytics, insurance operations, actuarial science or/and applied AI
Deep understanding of the insurance ecosystem, including medical cost drivers, regulatory frameworks, claims workflows and quality performance metrics
Proven track record of delivering data science and AI solutions that drive measurable financial and clinical impact
Solid expertise in predictive modeling, machine learning, data and analytical solution design
Expect level experience linking a business problem to applied data science techniques leading the hands-on technical talent in model development
Solid communication and partnership skills, with ability to align complex analytics to business and financial outcomes
Ability to demonstrate value-based leadership aligned to UnitedHealth Group culture and hire, grown and retain data science talent for future success

Responsibilities

UnitedHealthcare Strategy & Business Alignment
Collaborate with executive leaders across Financial, Actuarial, Healthcare Economics, Operations, Provider and Member Experience organizations to translate strategy into a prioritized data science roadmap with enhanced predictive capabilities
Identify and champion opportunities where AI and predictive analytics can improve care affordability, enhance population health management, optimize medical costs insights and signaling and streamline data and insights pipeline operations
Communicate analytical and predictive insights in clear, actionable terms to support strategic decisions and planning – not in technical terms
Value Creation in Focus
Lead development of value frameworks that quantify outcomes such as medical cost savings, improved quality scores, risk accuracy, utilization management efficiencies, financial forecasting, reserving and provider network performance
Ensure all predictive models and analytics directly improve key metrics and regulatory, legal and compliance performance
Applied AI Transformation Across the Insurance Lifecycle
Drive end-to-end AI/ML development across connected data domains of claims, member, provider and networks
Enhanced Forecasting Capabilities: Predictive models for early detection, medical cost trends, member behavior insights and member population stratification
Support Utilization Management & Care Management: Influence models to automate prior authorization, detect avoidable utilization and target members for care coordination
Provider Network Optimization: Predictive insights to manage provider performance, leakage, cost variation and contracting opportunities
Member Engagement & Experience: Personalization models that improve retention, adherence and satisfaction
Payment Integrity & FWA: Advanced anomaly detection and claims pattern analytics and predictions
Deliver high-quality and contemporary data science models that are explainable, compliant and integrated into real-world workflows
Operationalization & Cross-Functional Collaboration
Partner with Technology, Clinical Operations, Claims, Actuarial and Product teams to deploy and scale AI solutions across the enterprise
Embed data science resources within key business areas to maximize adoption and impact
Ensure seamless integration of models into products, workflows and decision-support systems
Leadership & Talent Development
Build and lead a high-performing, multidisciplinary team of data scientists, data engineering experts, machine learning engineers and AI product partners
Foster a culture of innovation, collaboration and influence grounded in healthcare ethics and member-first principles
Strengthen data literacy across UHC, empowering leaders to make informed decisions
Success Measures
Recognized improvement in medical cost trend forecasting and improved affordability
Increased automation and efficiency across data and insights operations
Scaled adoption of AI-enabled insights across business units
Strong governance, compliance readiness and responsible AI maturity
High engagement, retention and growth within the data science team
Technical Expertise
Machine Learning & Advanced Analytics
Predictive modeling (classification, regression, survival analysis)
Ensemble methods, gradient boosting, random forests and XGBoost/LightGBM
Deep learning (NLP, sequence models, representation learning)
Probabilistic modeling and Bayesian methods
Time-series forecasting (claims trend, utilization, membership, RAF forecasting)
Causal inference and uplift modeling for interventions and care programs
Productization of Data Science & AI
Building analytic products (risk engines, utilization predictors, network tools)
Designing feedback loops for continuous improvement
A/B testing and impact measurement in operational workflows
Integrating AI into digital member, provider experiences and internal workflows
Insurance Data Expertise
Claims data (medical, pharmacy, behavioral, dental)
EHR/clinical data, lab results, clinical notes
Risk adjustment models
Quality measurement frameworks
Social determinants of health data integration
Provider data: NPI, cost/quality variation, contracting data
Natural Language Processing (NLP)
Clinical document processing (progress notes, medical records, appeals)
Claims and prior authorization text extraction/automation
Named entity recognition for diagnoses, procedures, risk factors
Large language models (LLMs) for summarization, routing, code capture, member communication
AI/ML Engineering & Deployment
Model operationalization and MLOps
Feature stores, model registries, and artifact tracking
Model monitoring (drift, bias, performance, retraining)
Building scalable AI services/APIs for production
Real-time inference and decision-support integration
Cloud ML platforms knowledge
Data Engineering
Data warehousing and lakehouse platforms (Databricks, Snowflake knowledge)
Data pipelines for high-volume claims and clinical data
Data modeling for healthcare operations (member, provider, claims)
Preferred Programming & Tools knowledge
Python (pandas, scikit-learn, PyTorch, TensorFlow)
SAS (STAT, ETS and OR)
R (useful for actuarial, risk, quality analytics)
Spark/PySpark
Visualization and BI platforms (Power BI)