Real-World Data Technical Analyst (RWD)

Katalyst Healthcares & Life Sciences

Rahway, NJ

JOB DETAILS
SKILLS
Analysis Skills, Biology, Biostatistics, Clinical Research, Communication Skills, Cross-Functional, Data Analysis, Data Sets, Documentation, Ecosystems, Electronic Medical Records, Feasibility Analysis, Git, Healthcare, Medical Record System, MySQL, Oncology, Python Programming/Scripting Language, R Programming Language, SQL (Structured Query Language), Scientific Research, Source Code/Configuration Management (SCM), Statistical Analysis System (SAS), Team Player, Technical Analysis, Technical Support, Workflow Analysis
LOCATION
Rahway, NJ
POSTED
30+ days ago
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.

About the Company

K

Katalyst Healthcares & Life Sciences