Senior Research Data Analyst

Responsibilities

• Collaborate with cross-functional teams to design and execute observational research studies by developing SQL data pipelines and R-based statistical analyses.
• Architect end-to-end programming pipelines for large-scale healthcare data extraction, transformation, and validation using SQL.
• Lead implementation of analytical plans featuring complex statistical modeling in R.
• Validate analysis work, troubleshoot issues, and iterate on analytic approaches.
• Maintain high-quality standards through rigorous documentation, best coding practices, quality control, and version control with Git.
• Produce publication-ready deliverables.
• Present interim and final results to cross-functional stakeholders.
• Design and implement standardized programming modules to enhance reproducibility and efficiency.
• Support internal tool development and contribute to publications.

Requirements

• MS in Statistics, Biostatistics, Data Science, Public Health, Epidemiology, or related quantitative field.
• 5+ 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 real-world data (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 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; ability to manage concurrent projects.

Nice-to-Haves

• Advanced SQL skills (dbt, query optimization).
• Experience with advanced observational study designs.
• Extensive HEOR/RWE expertise on treatment pattern analysis, LoT, HCRU/cost analysis, synthetic control arm, and patient outcome research.
• Experience with health economic modeling, including Budget Impact Models (BIM) and Cost-Effectiveness Analysis (CEA).
• Advanced statistical modeling experience in PSM, survival analysis, causal inference, and bootstrapping.
• Contributions to peer-reviewed publications.
• Exposure to RESTful APIs, R package development, or Shiny apps.