Real-World Data Technical Analyst (RWD)

Real-World Data Technical Analyst (RWD) Rahway, NJ Job Details Contract 21 hours ago Qualifications Statistics Version control Cloud data warehouses SAS Scientific research RStudio Data reporting Git Mid-level SAS language Survival analysis

SQL EMR/EHR

Analysis skills Software documentation Epidemiology Technical writing Redshift Cross-functional collaboration Communication skills Technical Proficiency Python Cross-functional communication Database software proficiency

Full Job Description Job Description:

The Observational and Real-World Evidence (CORE) Real-World Data Analytics and Innovation (RDAI) team is seeking a Real-World Data (RWD) Technical Analyst to support real-world evidence generation and oncology outcomes research. This role will work with epidemiologists, biostatisticians, and scientists to conduct analyses using real-world data sources (claims, EHR/EMR, registries) and help develop advanced analytics tools and methodologies that accelerate observational research.

Responsibilities:

Conduct feasibility analyses using internal real-world datasets (claims, EHR/EMR) to support oncology outcomes research. Execute end-to-end study analyses using platforms such as RStudio and SAS Studio. Support development and implementation of analytics methods and tools to address confounding in observational healthcare data. Perform targeted literature reviews to support study design and methodology. Develop and maintain programming documentation, code specifications, and version control. Generate analytic outputs and reports supporting real-world evidence studies. Collaborate with cross-functional scientists to translate research questions into reproducible analytic workflows.

Requirements:

Experience working with real-world healthcare data (claims, EHR/EMR, registries). Strong understanding of epidemiologic or statistical methods for observational research. Proficiency in R, SAS, and SQL (Python a plus). Experience with R ecosystem tools (RStudio Workbench, RStudio Connect, RShiny). Familiarity with survival analysis methods and packages (e.g., survival). Experience working with databases (e.g., Redshift, MySQL). Experience with version control tools such as Git. Strong documentation, communication, and collaboration skills. Experience supporting life sciences or pharmaceutical research environments.