Data Scientist II will leverage advanced analytics, machine learning, and statistical methodologies to extract meaningful insights from healthcare and operational data at Nicklaus Children's Health System in Miami, Florida. The role focuses on analysis of electronic health records (EHR), genomics, precision medicine initiatives, financial data, and ERP data. The candidate will collaborate with researchers, clinicians, and operational teams, ensuring all data analysis activities comply with HIPAA regulations and health system policies. Responsibilities include applying machine learning, natural language processing, and AI techniques to structured and unstructured healthcare data; developing predictive models integrating multi-omics data to support precision medicine and personalized care pathways; building scalable data pipelines and connecting complex healthcare data using graph database technologies; collaborating with data engineering and IT teams to operationalize models for real-time decision support, risk stratification, and resource optimization; developing and validating predictive models to support clinical decision support and population health outcomes in pediatric settings; ensuring robust documentation and reproducibility of analyses; providing technical guidance and training on data analysis techniques; translating business requirements into technical solutions and communicating findings to both technical and non-technical stakeholders.

Data Scientist II will leverage advanced analytics, machine learning, and statistical methodologies to extract meaningful insights from healthcare and operational data at Nicklaus Children's Health System in Miami, Florida. The role focuses on analysis of electronic health records (EHR), genomics, precision medicine initiatives, financial data, and ERP data. The candidate will collaborate with researchers, clinicians, and operational teams, ensuring all data analysis activities comply with HIPAA regulations and health system policies. Responsibilities include applying machine learning, natural language processing, and AI techniques to structured and unstructured healthcare data; developing predictive models integrating multi-omics data to support precision medicine and personalized care pathways; building scalable data pipelines and connecting complex healthcare data using graph database technologies; collaborating with data engineering and IT teams to operationalize models for real-time decision support, risk stratification, and resource optimization; developing and validating predictive models to support clinical decision support and population health outcomes in pediatric settings; ensuring robust documentation and reproducibility of analyses; providing technical guidance and training on data analysis techniques; translating business requirements into technical solutions and communicating findings to both technical and non-technical stakeholders.