Machine Learning Engineer

Machine Learning Engineer - (Contract) Location: Remote - Must based in the US Duration: 6 months, with extension potential About the Role We’re partnering with a leading healthcare technology company to scale their machine learning capabilities. They’ve built a working Python classifier and now need an experienced Machine Learning Engineer to productionize, tune, and extend it. This role is highly hands-on and impact-driven: you’ll own model refinement, introduce anomaly detection for claims/cost data, and deploy into AWS SageMaker with version control and API access. You’ll work directly with the VP of Engineering, with clear scope and fast feedback. • What You’ll Do • Tune and validate an existing classification model, improving accuracy and explainability. • Implement anomaly detection for pharmaceutical claims/cost data. • Build light pipelines for data ingestion, training, and scoring. • Deploy and version models in AWS SageMaker, exposing results via API. Produce concise model reports (datasets, metrics, feature importance, overfitting analysis). What We’re Looking For • Proven experience as a Machine Learning Engineer delivering models into production. • Strong Python, scikit-learn, Pandas skills for data prep and model building. • Hands-on AWS SageMaker (training jobs, endpoints, versioning, deployment). • Practical experience with anomaly detection or fraud/risk/outlier modeling. • Solid statistics and evaluation methods (bias/variance, calibration, metrics). Comfortable working semi-independently, U.S. time-zone aligned. Nice to Have • Experience in healthcare, pharma claims, or financial risk analytics. • SQL/Postgres fluency. Familiarity with ML governance tools (MLflow, SageMaker Experiments). Why Join This is an opportunity to make an immediate impact - shaping ML models that directly influence real-world healthcare outcomes. You’ll work closely with leadership, ship quickly, and see your work move from prototype to production in weeks, not years.