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. Role - Senior Machine Learning Engineer Location - Remote / EST work hours Tentative Duration - 6 months 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.