Vice President, 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 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.

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)

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