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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Healthcare Data Scientist (Medicare/Medicaid) - **Company:** Jobgether - **Location:** Austria (Remote available) - **Experience:** Expert - **Salary:** €97,968.0 - €132,250.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Health Informatics, Computer Engineering, Data Infrastructure, Data Visualization, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Standard Sql, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Apache Spark, Model Validation, Generative AI, Pandas, Matplotlib, Scikit Learn, HuggingFace, Databricks - **Published:** September 2, 2026 - **Apply:** https://www.adzuna.at/details/5864763618 ## About the Role * Bachelor's degree in an analytical or technical discipline such as Statistics, Computer Engineering, Applied Mathematics, Data Science, or a related field, or equivalent relevant experience. * At least 5 years of professional experience in data science, including experience developing and applying advanced analytical models. * At least 2 years of hands-on experience analyzing Medicare and/or Medicaid data, with a strong understanding of healthcare claims and program data. * Proficiency in Python or R for data science and machine learning, with experience using tools such as Pandas, NumPy, TensorFlow, Scikit-learn, Seaborn, ggplot, or Matplotlib. * Working knowledge of SQL and modern data platforms and technologies such as Databricks, Snowflake, and Spark. * Hands-on experience with generative AI technologies and frameworks, such as LangChain, LangGraph, Hugging Face Transformers, vector databases, agent frameworks, or LLM orchestration tools. * Strong understanding of statistics, machine learning fundamentals, predictive modeling, feature engineering, and model evaluation principles. * Ability to lead analytical work independently while collaborating effectively across technical and business teams. * Strong visualization, communication, and storytelling skills, with the ability to simplify complex data and AI concepts for varied audiences. * A proactive, curious, self-directed mindset combined with strong problem-solving skills and a practical approach to applying emerging technologies. ## Description 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, model validation, and train/test methodologies. * Create clear and effective data visualizations that communicate complex findings and emerging trends. * Present analytical results and recommendations to both technical and non-technical stakeholders, including senior leadership. * Contribute to additional analytical, data science, and AI initiatives as required. ## Related Videos - [Vectorize all the things! 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