Machine Learning Engineer  (Contract)

Location: Remote - Must be 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.