Job Description

Roles & Responsibilities

• Build, train, test, and evaluate predictive machine learning models for healthcare use cases.
• Develop models that support clinical risk prediction, patient outcomes, utilisation, readmission risk, claims analytics, revenue cycle optimisation, and care pathway improvement.
• Analyse and model healthcare datasets including EHR, clinical, claims, pharmacy, laboratory, patient activity, and healthcare operational data.
• Apply statistical and machine learning techniques such as logistic regression, survival analysis, classification, clustering, random forest, XGBoost, and other predictive modelling methods.
• Prepare analytical datasets using advanced SQL, including cohort building, joins, window functions, longitudinal patient data, feature engineering, and data quality checks.
• Work with healthcare data standards and coding systems such as ICD-10/11, CPT, SNOMED CT, LOINC, HL7, and FHIR.
• Support clinical decision support, predictive analytics, healthcare AI, and data governance initiatives.
• Collaborate with clinicians and operational stakeholders to translate data science outputs into practical healthcare workflows.
• Evaluate model performance using appropriate metrics such as AUC, precision, recall, F1 score, sensitivity, specificity, calibration, and explainability methods.
• Ensure healthcare data privacy, ethical AI use, and compliance with relevant healthcare data governance requirements.