Vice President, Data Scientist – UnitedHealthcare

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 Vice President Data Scientist will join the newly created UnitedHealthcare Data Science Capability reporting to the Chief Data Scientist. This role will lead and participate in the development of new predictive techniques to address the business aligned use cases identified. This leader will partner cross-functionally with the broad UnitedHealth Group tech ecosystem. The Vice President will need excellent analytical thinking and problem solving skills to collaborate within the enterprise architecture while designing new data solutions and productized approach to data science. The leader will oversee a small, dedicated team of contemporary data scientists and work with the broader analytics teams within finance, actuarial and healthcare economics teams. 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.

Responsibilities

• Collaborate with business leaders to problem solve the analytical approach to new and enhanced insights resting on advanced data science techniques
• Develop and implement predictive models, machine learning algorithms and statistical analyses to solve complex business problems
• Design experiments and evaluate model performance using appropriate validations techniques
• Lead the coding and peer review process of predictive techniques to secure explainable and ethical modeling design
• Detailed-oriented approach to use machine learning methods designed to uncover new insights and maximize the value of data available across data domains
• Develop data pipelines and automate repetitive analytics processes for optimized business efficiencies
• Stay current on emerging techniques, technologies and best practices in data science and AI
• Spearhead and influence community of practice across the enterprise by positive interactions and knowledge sharing
• Build and lead a high-performing, multidisciplinary team of data scientists and machine learning engineers and AI product partners

Requirements

• 10+ years of proven “hands-on” data science modeling experience in an applied field
• Experience in healthcare analytics, insurance operations, actuarial science or applied AI
• 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 impact and scale
• All employees working remotely will be required to adhere to UnitedHealth Group’s Telecommuter Policy

Nice-to-haves

• Machine Learning & Advanced Analytics
• Predictive modeling (classification, regression, survival analysis)
• Ensemble methods, gradient boosting, random forests, 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)

Benefits

• a comprehensive benefits package
• incentive and recognition programs
• equity stock purchase
• 401k contribution

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