Data Scientist - Healthcare Fraud Waste and Abuse

About the role

This role focuses on identifying and preventing fraud, waste, and abuse within healthcare claims data. You will develop and implement advanced analytical models to uncover financial anomalies and drive cost savings. The position is a contract-to-hire opportunity with a path to full-time employment.

Key facts

What you'll do

• Construct and deploy predictive models to detect unusual patterns and anomalies in healthcare financial and claims data, specifically related to fraud, waste, and abuse.
• Create and manage sophisticated data solutions designed to automatically detect and mitigate fraud, waste, and abuse.
• Build data processing workflows for structured, text, and document data, optimized for large language models and multi-modal AI.
• Design, develop, and deploy AI solutions utilizing techniques like Retrieval Augmented Generation (RAG) and embeddings for claims processing and document analysis.
• Perform in-depth data analysis, extensive SQL querying, and predictive modeling to identify trends.
• Work with AI agent tools to implement and maintain advanced operational processes that integrate third-party solutions.
• Communicate findings and recommendations to leadership, including department heads and business leaders, to embed automated data processes into revenue cycle operations.
• Present complex data insights through visualizations and dashboards to both technical and non-technical audiences, including legal teams.

Requirements

• Bachelor of Science degree.
• Minimum of 2 years of experience in the healthcare sector, with a focus on revenue cycle, finance, or fraud, waste, and abuse.
• Solid understanding of applied statistics, regression analysis, and clustering methods.
• Proficient in Python and its associated libraries, including PySpark, Numpy, and SciPy.
• Advanced skills in SQL.

Nice to have

• Experience with LLM-based solutions such as RAG, embeddings, and instruction tuning.
• Familiarity with Agentic AI tools and workflow automation.

Skills & tools

• Python (PySpark, Numpy, SciPy)
• SQL
• Applied Statistics
• Regression Analysis
• Clustering Techniques
• Large Language Models (LLMs)
• RAG
• Embeddings
• Agentic AI

Practical notes

• This is a contract to hire position.
• No C2C inquiries.
• Background check required.