Healthcare ML Engineer: Data Pipelines & Models

Position Overview: We are looking for a talented Machine Learning Engineer to help build and maintain machine learning pipelines that support predictive healthcare models. This role is ideal for someone with strong data engineering skills and a passion for applying machine learning to real-world healthcare challenges. Key Responsibilities: Design and implement data pipelines using SQL and Python from various data sources. Synthesize large medical claims data records into useable data features. Collaborate with Data Scientists to build, refine, and deploy various machine learning models in Python. Learn and apply SAS as needed for dataset generation (prior experience is not required). Ensure data quality, reproducibility, and performance in the pipelines. Contribute to best practices for data governance and model deployment. Qualifications: 2-5+ YOE as a Machine Learning Engineer or Data Scientist with a focus on engineering data builds. Proficiency in SQL/Snowflake and Python; willingness to learn SAS as needed. Experience building and combining data pipelines and working with large datasets. Basic understanding of healthcare claims data (e.g., Medicare claims) preferred. Familiarity with machine learning workflows (e.g., scikit-learn, pandas, NumPy). Strong problem-solving and communication skills, sharp critical thinking skills. Comfortable with a fast-paced workstream that requires flexibility to work on multiple projects or analyses at the same time. #J-18808-Ljbffr Silversmith Capital Partners