Chief Data Scientist - UnitedHealthcare

At United Healthcare, 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 United Healthcare 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 United Healthcare business leaders to identify high-impact transformative opportunities and deliver solutions that drive impact. The senior leader will work cross functionally with the United Health 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.

• 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

• 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

• 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

• 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

• 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

• 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

• 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…