Transform complex healthcare data into actionable insights that improve patient outcomes by analyzing massive datasets from electronic health records, claims systems, and medical devices to uncover patterns, build predictive models, and inform strategic decisions.
Requirements
- Expert proficiency in Python (pandas, NumPy, scikit-learn) or R (tidyverse, caret)
- Advanced SQL skills for complex queries across relational databases
- Experience with statistical analysis: hypothesis testing, regression modeling, survival analysis
- Proficiency with ML libraries: scikit-learn, XGBoost, LightGBM
- Expert-level data visualization skills using Tableau, Power BI, or matplotlib/ggplot2
- Understanding of healthcare data structures: EHR schemas, claims formats (837, FHIR)
- Knowledge of medical coding systems: ICD-10, CPT, SNOMED, LOINC
Responsibilities
- Analyze complex healthcare datasets including EHRs (Epic, Cerner), claims (CMS, commercial payers), and patient-generated data
- Conduct exploratory data analysis to identify trends, anomalies, and opportunities for AI-driven interventions
- Build dashboards and visualizations that communicate complex findings to non-technical stakeholders
- Perform statistical analysis to validate hypotheses and measure the impact of healthcare interventions
- Develop predictive models for clinical outcomes: readmission risk, disease progression, treatment response
- Build risk stratification models to identify high-risk patient populations for preventive interventions
- Create propensity score models for causal inference in observational healthcare data
Other
- Masters degree in Data Science, Biostatistics, Epidemiology, Health Informatics, or related quantitative field
- 3+ years of experience as a data scientist, preferably in healthcare or life sciences
- Proven experience analyzing healthcare data (EHRs, claims, clinical trials, population health)
- Track record of delivering data science projects from ambiguous requirements to actionable insights
- Experience presenting analytical findings to executive leadership and clinical stakeholders
