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13 days ago
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Requirements and responsibilities
Build a drug access platform for a fast-growing, profitable health-tech company, where your machine learning models directly detect and prevent drug-access failures, helping millions of insured Americans actually receive the medications their benefits promise.We’re hiring a Machine Learning Engineer to work on applied predictive modeling and anomaly detection across complex, high-volume healthcare program data. This is a hands-on, senior IC role focused on turning messy real-world signals into clear, actionable insights that improve medication access across the U.S. healthcare system.Unlike academic or toy ML problems, the work here sits directly in the flow of patient access: identifying failures in drug affordability programs, surfacing non-obvious breakdowns in benefit usage, and enabling teams to intervene before patients abandon treatment.What You’ll DoBuild predictive models (forecasting, time series, trend analysis) on real-world program and financial data.Design anomaly / outlier detection to surface risks, inefficiencies, and non-obvious patterns.Own feature engineering, model evaluation, and interpretability (feature importance, SHAP-style analysis).Work end-to-end from exploration → modeling → business-facing insights.Partner with product and analytics teams to turn models into decisions.Must-Have4–7 years as ML Engineer or Applied Data ScientistStrong predictive modeling + anomaly detection backgroundHands-on experience with messy, high-volume financial or program dataStrong statistical intuition (not just fitting models)Python with Pandas / NumPy / scikit-learnComfortable operating independently as a senior ICNice to HaveU.S. healthcare-adjacent data (insurance, benefits, copay, utilization)Experience in compliance-aware environments (HIPAA-constrained data)Translating models into dashboards, reports, or exec-ready insightsXGBoost / LightGBM, solid SQLExplicitly Not RequiredMLOps ownership or DevOps workManaging teamsPharma biology expertise
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