Senior Analyst - Data Science

• Model Development Prototyping: Design, train, and validate machine learning and statistical models focused on risk stratification, disease progression, treatment intelligence, and healthcare recommendations.
• Longitudinal EHR Feature Engineering: Translate temporal clinical touchpointssuch as diagnostic records, symptoms, lab investigations, and prescriptionsinto robust features for predictive models and generative AI systems.
• Unstructured Data Pipeline Design: Build models and pipelines capable of extracting insights from unstructured healthcare records, doctor notes, and medical terminologies.
• Problem Scoping Hypothesis Testing: Frame open-ended clinical questions into clear, measurable machine learning objectives; define appropriate targets, data requirements, and success metrics.
• Production Integration Monitoring: Work alongside engineering teams to operationalize prototypes into production environments and establish continuous model performance tracking.
• Comprehensive Model Evaluation: Construct evaluation systems that assess offline metrics alongside real-world clinical validity, model calibration, algorithmic bias, and robustness.

• Machine Learning Statistical Modeling (Model Design, Evaluation, Calibration)
• Python Core Data Science Stack (Pandas, NumPy, Scikit-learn, PyTorch or similar)
• Advanced SQL Complex Querying
• Working with Large-Scale, Messy Longitudinal Data
• Problem Formulation (Translating ambiguous domain/business problems into concrete ML setups)

• Healthcare/EHR/Claims Data Processing Clinical Ontologies
• Clinical NLP Medical Language Models (including LLMs/Generative AI)
• Advanced Analytics: Time-Series Analysis, Survival Analysis, Causal Inference
• Big Data Frameworks (e.g., PySpark)
• Production ML Deployment Performance Monitoring
• Technical Communication Cross-Functional Stakeholder Alignment

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