Sr. Healthcare Data Scientist (Medicare/Medicaid)

We are GDIT. We take pride in providing our clients with the data they need to make important decisions that impact the world around us.

You make GDIT your place by delivering insights to help our clients make impactful changes real. By owning your opportunity at GDIT, you’ll become a critical part in how we successfully solve our clients’ biggest challenges.

Our work is looking for a Sr. Data Scientist with deep experience in advanced analytics and modern AI technologies. In this role, you will help evolve the analytical capabilities used to detect and prevent fraud, waste, and abuse (FWA) in the Medicare and Medicaid programs. This includes working with cutting-edge generative AI models, AI agents, and scalable data science tools. A typical day will include:

• Analyzing Medicare and Medicaid claims data using statistical and machine learning techniques.
• Developing generative AI–powered solutions and agent-based systems to enhance fraud detection, investigate complex patterns, and optimize business processes.
• Building predictive and anomaly detection models to improve insights, efficiencies, and program integrity outcomes.
• Identifying opportunities to leverage large-scale CMS data with modern AI/ML methods, including LLMs, embeddings, vector databases, and retrieval-augmented pipelines.
• Present findings to a range of audiences (technical/non-technical) and to various levels of leadership.
• Performs additional duties as assigned.

Required Skills:

• Bachelors degree with an analytical or technical focus (i.e., Statistics, Computer Engineering, Applied Mathematics) or related area
• Minimum 5 years of experience in Data Science
• Minimum of 2 years of experience conducting advanced analyses of Medicare and/or Medicaid data
• Familiarity with R or Python for data science (including machine learning)
• Familiarity and experience with one or more of the following data science packages: Pandas, NumPy, Tensorflow, Scikit learn, Seaborn, GGplot, Matplotlib
• Familiarity with SQL, Databricks, Snowflake and Spark languages/technologies
• Hands-on experience with generative AI technologies such as LangChain, LangGraph, Hugging Face Transformers, vector databases, agent frameworks, or LLM orchestration tools.
• Understanding of statistics and basic analytic model development principles such as feature engineering, attribute selection, threshold setting, train/test split, etc.
• Ability to create data visualizations to present complex analyses and to illustrate trends
• A self-starter mindset with strong curiosity, problem‑solving abilities, and the ability to simplify and explain complex data and AI systems