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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in causal inference techniques and observational study design, with a strong proficiency in Python, R, and SQL for analyzing healthcare data. Capable of developing rigorous statistical methodologies and presenting complex findings to diverse stakeholders.
Highest-signal resume keywords
Causal Inference TechniquesHealthcare Claims AnalysisStatistical Methodologies DevelopmentPython ProficiencyEHR Data Analysis
ATS Keywords
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Hard Skills
BiostatisticsEpidemiologyHealth EconomicsStatistical ValidationPropensity Score MatchingQuasi-Experimental ResearchConfounding AdjustmentAnalytical Study DesignData AnalysisSensitivity Analysis
Soft Skills
Excellent Communication SkillsPresentation SkillsCollaboration
Tools & Technologies
PythonRSQLDbtAmazon RedshiftLooker
Certifications & Qualifications
Advanced Degree in StatisticsAdvanced Degree in Data ScienceAdvanced Degree in BiostatisticsAdvanced Degree in EpidemiologyAdvanced Degree in Health Economics
Industry Keywords
Healthcare Data PrivacyHIPAA ComplianceAPCMCCMHealthcare Reimbursement Models
Tech Stack
Tools & technologiesAmazon RedshiftPythonSQL
About the role
Key responsibilities & impact- Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies
- Evaluate the effectiveness of APCM and CCM programs using healthcare claims and EHR data
- Establish patient cohorts, comparison groups, outcome definitions, and analytical frameworks
- Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes
- Develop statistically rigorous, reproducible methodologies suitable for actuarial and clinical review
- Apply causal inference techniques, including propensity score matching, inverse probability weighting, and difference-in-differences
- Identify and address confounding variables, selection bias, and limitations in observational healthcare data
- Develop high-dimensional statistical matching approaches for large, complex datasets
- Conduct sensitivity analyses and validate research findings
- Analyze healthcare claims, EHR, and patient utilization datasets
- Develop and maintain analytical workflows using Python, R, and SQL
- Work within data environments including dbt, Amazon Redshift, and Looker
- Ensure data integrity, analytical reproducibility, and compliance with PHI security requirements
- Produce documented analytical code and reporting assets
- Collaborate with the Director of Business Intelligence and internal stakeholders
- Engage with healthcare clients, clinical leadership, and actuarial teams
- Present statistical findings and clinical outcomes clearly
- Develop executive-ready presentations, reports, and supporting documentation
- Translate complex findings into business and clinical insights
- Review previous vendors’ analytical deliverables and identify reusable methodologies, data assets, and findings
- Recommend improvements to analytical processes and study designs
- Document methodologies and transfer knowledge to the internal Business Intelligence team
Requirements
What you’ll need- Extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research
- Demonstrated expertise in causal inference and observational study design
- Proven experience evaluating healthcare interventions and measuring clinical outcomes
- Strong proficiency in Python, R, and SQL
- Experience analyzing large-scale healthcare claims and EHR datasets
- Deep understanding of propensity score matching, quasi-experimental research methodologies, statistical validation, and confounding adjustment
- Ability to independently design and execute technically sophisticated analytical studies
- Experience developing statistical methodologies suitable for rigorous clinical, actuarial, or external stakeholder review
- Strong understanding of healthcare data privacy, HIPAA, and protected health information requirements
- Excellent communication skills, including the ability to present complex findings to nontechnical stakeholders
- Advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline
- Familiarity with APCM, CCM, and healthcare reimbursement models
- Knowledge of dbt, Amazon Redshift, and Looker
- Experience supporting health-tech startups or rapidly growing healthcare technology companies
- Experience developing executive-level clinical outcomes presentations and client-facing research reports
Benefits
Comp & perks- Flexible, part-time engagement
- Fully remote work arrangement
- Flexible schedule
- Opportunity to increase weekly hours as project requirements evolve
- Project-based engagement with possibility of extension
- Opportunity to contribute to meaningful healthcare research and measurable improvements in patient outcomes