ML Data Engineer

About the job

Role Overview: You will report to the Data Science Enablement Manager and support the Rare Disease Business Unit (RDBU) patient finding team by working closely with data scientists. Your responsibilities will include building scalable pipelines, productionizing machine learning models, designing evaluation and monitoring frameworks, enabling ML lifecycle management, translating analytical solutions into production-ready systems, developing ML-ready datasets, supporting model tracking and experiment management, ensuring compliance with data governance standards, and following engineering best practices for code quality and testing. Key Responsibilities: - Build and maintain scalable data and ML pipelines for patient finding use cases. - Productionize machine learning models by developing deployment workflows and scoring pipelines. - Design and implement model evaluation, validation, and monitoring frameworks. - Enable end-to-end ML lifecycle management, including training, versioning, deployment, and retraining workflows. - Partner with RDBU data science teams to translate analytical solutions into production-ready systems. - Develop ML-ready datasets and feature pipelines with a focus on data quality and reusability. - Support model tracking and experiment management using standardized tools and frameworks. - Collaborate with enterprise data and platform teams to ensure compliance with data governance and security standards. - Follow engineering best practices for code quality, documentation, testing, and CI/CD integration.