Senior Machine Learning Engineer

As aSenior Machine Learning Engineer, you will focus on the design and implementation of high-performance predictive models using structured and semi-structured healthcare data. You will bridge the gap between statistical research and production-grade software, turning raw clinical and claims data into actionable predictions that improve patient outcomes and operational efficiency.

• Model Development: Design, train, and optimize classical ML models (Classification, Regression, Clustering, Time-Series) for healthcare use cases.
• Feature Engineering: Architect and maintain advanced feature pipelines and feature stores to ensure the most relevant signals are extracted from complex claims data.
• End-to-End Deployment: Manage the full ML lifecycle, from exploratory data analysis (EDA) and hyperparameter tuning to deploying models as high-availability microservices.
• Software Engineering: Write robust, production-ready Python code, ensuring that ML components integrate seamlessly with our core healthcare platforms.
• Performance & Validation: Conduct rigorous statistical validation of models to ensure accuracy, fairness, and the absence of bias, particularly in clinical decision-support tools.
• On-Call & MLOps: Participate in an on-call rotation to support production pipelines. Advance CI/CD frameworks specifically for ML (Model CI), ensuring seamless retraining and redeployment.
• Compliance: Lead audits and ensure all ML workflows strictly adhere to HIPAA and Inovalon's internal data governance policies.

• Experience: Minimum 8 years of total experience, with 4+ years focused on building and deploying classical ML models in a production environment.
• Technical Stack: Proficiency in Python and standard ML libraries (e.g., scikit-learn, XGBoost, LightGBM, Statsmodels).
• Deep Statistical Knowledge: Strong understanding of statistical significance, hypothesis testing, and model interpretability (e.g., SHAP, LIME).
• Data Tools: Experience with SQL and Big Data technologies (e.g., Spark/PySpark, Hadoop) for processing large-scale tabular datasets.
• Cloud & MLOps: Proficiency with AWS/GCP/Azure and containerization (Docker, Kubernetes). Experience with workflow orchestration like Airflow or Prefect.
• Architecture: Proven track record with Feature Stores and automated model monitoring/drift detection.
• Domain Knowledge: Familiarity with healthcare standards (HL7, FHIR, ICD-10 codes) and HIPAA regulations is a significant plus.

• Analytical Rigor: A data-first mindset that prioritizes statistical validity over "black-box" approaches.
• Mentorship: Experience guiding junior engineers in best practices for code reviews and experiment tracking.
• Collaborative Spirit: The ability to explain complex model outputs to non-technical stakeholders (Product Managers, Clinicians).