Leveraging advanced analytics, machine learning and statistical methodologies to extract meaningful insights from healthcare and operational data at Nicklaus Children's Health System.
Requirements
- Proficiency in programming languages such as Python, R, and SQL for data analysis and modeling is required.
- Strong knowledge of statistical modeling, machine learning, and data visualization techniques.
- Familiarity with LLMs and foundation models for natural language processing and clinical data analytics.
- Familiarity with graph data platforms (e.g., Neo4j, Amazon Neptune) and vector databases (e.g., Pinecone).
- Experience with distributed computing, big data frameworks, and cloud platforms (e.g., Azure, AWS, GCP).
- Familiarity with healthcare information systems and EHR platforms.
- Experience with data integration from multiple healthcare sources.
Responsibilities
- Applies machine learning, natural language processing, and AI techniques to analyze structured and unstructured healthcare data, including clinical, genomics, and operational datasets.
- Develops predictive models integrating multi-omics data (genomics, proteomics), biomarkers, and clinical outcomes to support precision medicine and personalized care pathways.
- Builds scalable data pipelines and connects complex healthcare data using graph database technologies (e.g., property graphs) to uncover hidden relationships across patients, treatments, and outcomes.
- Collaborates with data engineering and IT teams to operationalize models for real-time decision support, risk stratification, and resource optimization across clinical and business operations.
- Develops and validates predictive models to support clinical decision support, risk stratification, and outcome prediction for pediatric patient populations.
- Ensures all analytical activities comply with healthcare regulations, institutional policies, and research ethics requirements, including proper handling of protected health information.
- Maintains comprehensive documentation of analytical methods, validate analytical approaches, and ensure reproducibility of research findings.
Other
- 3-5 years of experience in data science with demonstrated experience in healthcare or life sciences environments.
- Experience working with healthcare data, including electronic health records (EHR), clinical databases and/or ERP data.
- Proven track record of applying statistical analysis and machine learning techniques to real-world healthcare and healthcare operational problems.
- Understanding of healthcare regulations including HIPAA, FDA guidelines, and clinical research compliance requirements.
- Familiarity with medical coding systems (ICD-10, CPT, SNOMED).
