Sr. Healthcare Data Scientist

07 set - Italia
Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. Healthcare Data Scientist (Medicare/Medicaid) based in Italy.

This role offers the opportunity to apply advanced data science and AI to some of the most complex challenges in healthcare program integrity.
You will analyze large-scale Medicare and Medicaid claims data to uncover patterns associated with fraud, waste, and abuse.
The position combines statistical modeling, machine learning, generative AI, and agent-based technologies to create innovative analytical solutions.
You will work with modern tools including LLMs, embeddings, vector databases, and retrieval-augmented pipelines.
Your insights will support better decision-making, operational efficiency, and stronger outcomes across healthcare programs.
The environment is collaborative and mission-driven, with opportunities to translate sophisticated technical work into clear recommendations for diverse audiences.
This is an ideal opportunity for a curious,



hands-on data scientist who wants to make meaningful impact through healthcare analytics and emerging AI technologies.

Accountabilities
- Analyze Medicare and Medicaid claims data using statistical methods, machine learning, and advanced analytical techniques to identify trends, anomalies, and potential fraud, waste, and abuse.
- Develop generative AI-powered solutions and agent-based systems to strengthen fraud detection, investigate complex patterns, and improve business processes.
- Build predictive, anomaly detection, and other analytical models that improve program integrity, operational efficiency, and actionable insights.
- Identify opportunities to apply large-scale CMS data with modern AI and machine learning approaches, including LLMs, embeddings, vector databases, and retrieval-augmented generation pipelines.
- Apply sound data science practices including feature engineering, attribute selection, threshold setting,

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