Data Science/ML Engineer

We are seeking a highly skilled Senior Machine Learning Engineer to join the Foundational Data Analytics (FDA) Program, a strategic initiative focused on building a modern, intelligent data ecosystem. This role will lead the design, development, and deployment of machine learning models that support enterprise-wide analytics, predictive insights, and healthcare transformation.

FDA Program Objectives:

• Single Source of Truth (SSOT): Build standardized, high-quality data infrastructure ensuring consistency and reliability.
• Multifunctional Version of Truth (MVOT): Enable agile integration of diverse data sources for dynamic analytics.
• Centralized Business Intelligence: Deliver automated, real-time insights to optimize cost, quality, and performance.
• Capability Building: Strengthen population health, member engagement, quality improvement, revenue optimization, total cost of care, and compliance/risk functions.

Key Responsibilities:

• Design and implement scalable machine learning pipelines using structured and unstructured healthcare data.
• Collaborate with data engineers, architects, and business stakeholders to identify opportunities for predictive modeling and advanced analytics.
• Develop and deploy models for risk stratification, member engagement, cost prediction, and quality improvement.
• Ensure model interpretability, fairness, and compliance with healthcare regulations (e.g., HIPAA).
• Monitor model performance and retrain as needed to maintain accuracy and relevance.
• Contribute to the development of reusable ML components and frameworks within the FDA platform.
• Mentor junior ML engineers and data scientists.

Required Skills & Qualifications:

• Bachelor s or master s degree in computer science, Data Science, or related field.
• 8+ years of experience in machine learning engineering or applied data science.
• Proficiency in Python, SQL, and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with cloud platforms (Azure preferred) and tools like Databricks, MLflow, or Azure ML Studio.
• Strong understanding of data preprocessing, feature engineering, and model evaluation.
• Familiarity with healthcare data standards (FHIR, HL7) and payer systems.
• Excellent communication and collaboration skills.