AIML + Databricks

Responsibilities

• Develop machine learning, deep learning, and Generative AI models to support RCM use cases such as claim outcome prediction, denial classification, next-best-action recommendations, clinical appeal summarization, and workflow optimization.
• Build end-to-end ML pipelines, including feature engineering, model training, hyperparameter tuning, validation, and monitoring.
• Research and apply advanced techniques in LLMs, embeddings, Retrieval-Augmented Generation (RAG), prompt engineering, and document intelligence.
• Translate operational and product requirements into measurable model objectives, data specifications, and evaluation frameworks.
• Conduct Exploratory Data Analysis (EDA) to identify data patterns, anomalies, and business insights.
• Implement MLOps best practices, including model versioning, reproducibility, feature stores, and drift monitoring.
• Partner with Data Engineers to ensure feature availability, data quality, and scalable ML/AI deployment within Azure Databricks and enterprise platforms.
• Collaborate with cross-functional teams to communicate insights, present model results, and drive adoption of ML solutions.
• Stay updated on emerging AI technologies and propose enhancements or new opportunities for Provider Engineering platforms.Required Qualifications
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
• 3–7+ years of hands-on experience developing ML/AI models in an applied industry environment. Healthcare experience is strongly preferred.
• Strong proficiency in:
• Databricks
• Python (Pandas, Scikit-learn, NumPy, TensorFlow, MLflow)
• SQL
• Strong working knowledge of GitHub Copilot or an equivalent enterprise-approved AI coding assistant.
• Experience working with Azure or similar cloud environments, preferably:
• Azure Databricks (MLflow, Delta Lake)
• Azure Machine Learning
• Azure Data Lake / Azure Data Factory
• Hands-on experience in at least one of the following:
• Classification
• Regression
• Time Series
• Recommendation Systems
• Agentic AI / LLMs / RAG
• Strong statistical modeling foundation with experience building production-quality ML systems.
• Preferred: Knowledge of US healthcare, particularly Revenue Cycle Management (RCM), including familiarity with EDI transactions such as 837 and 835.
• Ability to work effectively in a fast-paced environment involving rapid prototyping, ambiguity, and iterative delivery.
• Self-motivated and solution-oriented, with the ability to independently research, troubleshoot, and identify solutions to technical or data challenges.