[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL]

PROFESSIONAL SUMMARY
Healthcare Data Scientist with [X] years applying machine learning to clinical and operational healthcare challenges. Expert in predictive modeling for patient outcomes, medical imaging analysis, and EHR data. Strong understanding of HIPAA compliance, clinical workflows, and healthcare regulations.

TECHNICAL SKILLS
Healthcare Domain: EHR/EMR Systems, Clinical Data (ICD-10, CPT, SNOMED), Medical Imaging (DICOM), HIPAA Compliance
ML/Analytics: Python, R, TensorFlow, PyTorch, Scikit-learn, Survival Analysis
Healthcare Tools: Epic, Cerner, HL7, FHIR, RedCap
Specializations: Risk Stratification, Clinical Decision Support, Population Health, Predictive Analytics
Visualization: Tableau, Power BI, R Shiny

PROFESSIONAL EXPERIENCE

Healthcare Data Scientist / Senior Data Scientist
[Healthcare Organization/Company Name], [City, State]
[Start Date] – Present
• Developed patient readmission prediction model reducing 30-day readmissions by [X%]
• Built sepsis prediction system providing [Y] hour early warning with [Z%] sensitivity
• Created risk stratification model identifying high-risk patients for care management programs
• Analyzed EHR data for [X]K patients improving clinical pathways and reducing LOS by [Y%]
• Collaborated with clinicians to deploy [number] clinical decision support tools
• Ensured HIPAA compliance and data privacy in all model development and deployment

Healthcare Data Scientist
[Previous Organization], [City, State]
[Start Date] – [End Date]
• Built no-show prediction model reducing appointment gaps by [X%]
• Developed medication adherence model identifying at-risk patients with [Y%] accuracy
• Analyzed claims data uncovering $[amount] in cost-saving opportunities
• Created population health dashboards tracking quality measures for [X]K patients
• Conducted survival analysis for [specific clinical outcome]

CLINICAL IMPACT PROJECTS

Readmission Risk Model
• Developed model predicting 30-day readmission with [X%] AUC
• Incorporated [number] clinical features from EHR data
• Deployed in Epic workflow reaching [Y] clinicians
• Impact: Reduced readmissions by [Z%] saving $[amount] annually

Disease Progression Prediction
• Built model forecasting [disease] progression using longitudinal EHR data
• Applied survival analysis and time series methods
• Enabled early intervention reducing complications by [X%]

Medical Imaging Analysis
• Developed CNN for [X-ray, CT, MRI] analysis achieving [accuracy metric]
• Reduced radiologist reading time by [Y%] while maintaining diagnostic accuracy
• Processed [X]K images with [deployment details]

EDUCATION
[Advanced Degree], [Biostatistics, Bioinformatics, Health Informatics, etc.]
[University Name], [Year]

[Degree], [Field]
[University Name], [Year]

PUBLICATIONS
• [Healthcare analytics paper], [Medical Journal], [Year]
• [Clinical decision support study], [Conference], [Year]

CERTIFICATIONS
• [Healthcare Data Analytics Certification], [Year]
• [HIPAA Training Certification], [Year]

DOMAIN EXPERTISE
• Clinical Knowledge: Understanding of clinical workflows, medical terminology, disease processes
• Healthcare Metrics: Length of stay, readmission rates, mortality, patient satisfaction
• Regulatory: HIPAA, HITECH, meaningful use requirements
• Data Standards: HL7, FHIR, ICD-10, CPT, LOINC, SNOMED CT