Role Overview

As a Medicare Risk Adjustment Data Scientist, you will play a critical role in developing, enhancing, and operationalizing AI/ML solutions that support Medicare Advantage risk adjustment programs. This role combines strong analytical thinking with applied machine learning and close collaboration with business and clinical stakeholders. This position sits within OI Clinical Solutions - Decision Intelligence. Upon selection, you will be part of a high impact team delivering best in class analytics and AI solutions aligned to CMS Medicare Advantage business needs.

Responsibilities

Partner with business, clinical, and technical stakeholders to translate business problems into data science solutions and analytical frameworks
Develop actionable insights and clearly communicate findings and recommendations to internal and external stakeholders, including leadership
Design, develop, and evaluate machine learning models (predictive and prescriptive) using structured and unstructured healthcare data
Apply AI/ML techniques across use cases such as risk adjustment analytics, NLP based chart review, and documentation analysis
Perform feature engineering, model training, validation, and performance evaluation to ensure robust and explainable outcomes
Leverage UHG's big data ecosystem (Python, PySpark, Spark, Hive, Cloud platforms) to solve large scale analytical problems
Collaborate with data engineering and platform teams to ensure models are production ready and scalable
Stay current with emerging tools, techniques, and methodologies in AI/ML, NLP, and Generative AI
Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives.

Requirements

Required Qualifications:

Bachelor's or Master's degree in Engineering, Computer Science, Mathematics, Statistics, Economics, or a related quantitative field
2+ years of hands on experience in Data Science / AI / Machine Learning roles
Hands on experience with Deep Learning techniques (e.g., CNNs, RNNs, Transformers) applied to real world problems
Experience in Natural Language Processing (NLP) including text classification, NER, summarization, or similar tasks
Solid foundation in machine learning techniques such as Logistic Regression, Random Forest, Gradient Boosting (XGBoost), SVM, etc
Good working knowledge of LLMs and Generative AI, including applied use cases
Solid programming skills in Python / PySpark
Proven excellent analytical, problem solving, and communication skills, with the ability to present insights to non technical audiences

Preferred Qualifications:

2+ years of experience using Python, Spark, and Hive in large scale analytics or ML environments
2+ years of hands on experience developing and evaluating AI/ML models
Practical experience applying Deep Learning and NLP techniques
Working experience on cloud platforms, preferably Azure, including familiarity with cloud based data storage and compute services used for analytics and ML workloads
Understanding of Medicare Risk Adjustment programs and workflows
Familiarity with CMS datasets such as MMR, MOR, EDPS, or related data sources