Senior Research Data Analyst, Komodo Health

• BS/MS in Statistics, Biostatistics, Data Science, Public Health, Epidemiology, or a related quantitative field
• 3+ years of industry experience developing production-grade SQL pipelines (querying, cleaning, transformation) for large-scale databases
• Proficient in R (preferred), Python, or SAS for statistical programming
• Experience with Git version control and command-line workflows
• Hands-on experience with administrative claims, EHR, or equivalent RWD. Familiarity with healthcare data models and U.S. medical coding systems (ICD-10, CPT, NDC)
• Experience collaborating with epidemiologists/SMEs to execute research studies, with a solid understanding of epidemiological principles
• Proven ability to implement statistical analysis (descriptive analytics, regression, hypothesis testing, and visualization)
• Adaptability to learn new tools and methods in fast-paced environments; ability to manage concurrent projects with competing deadlines

Desirable

• This role is critical to designing and implementing data processing and analysis programming pipelines for Life Science/HEOR/RWE research studies utilizing the Komodo Healthcare Map
• Collaborate with cross-functional teams to design and execute observational research studies, by developing SQL data pipelines and R-based statistical analyses
• Validate analysis work, troubleshoot issues, and iterate on analytic approaches to ensure alignment with research protocols and study objectives
• Support internal tool development and contribute to publications to enhance Komodo's scientific presence
• Looking back on your first 12 months at Komodo Health, you will have…
• Architected end-to-end programming pipelines for large-scale healthcare data extraction, transformation, and validation using SQL to power observational research studies leveraging Komodo Healthcare Map
• Led the implementation of analytical plans featuring complex statistical modeling in R, aligning research objectives with appropriate methodological approaches to support HEOR/RWE studies
• Maintained high-quality standards through rigorous documentation, best coding practices, quality control processes, and version control with Git throughout the study lifecycle
• Produced publication-ready deliverables meeting industry standards for external clients
• Presented interim and final results to cross-functional stakeholders, incorporating feedback to refine analyses
• Designed and implemented standardized programming modules to enhance research reproducibility and analytical efficiency across projects