Real-World Data Technical Analyst (RWD)

Katalyst Talent Agency

Rahway, NJ, United States

15 days ago

Apply on www.careerjet.com

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Amazon Web Services Databases Extract Transform Load (ETL) R (Programming Language) Revision Control Systems Python (Programming Language) MySQL RStudio SAS (Software) SQL Databases Git Data Analytics

+1 more

Software Version Control

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., * 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., Role: Senior Technical Analyst Location: WestPoint/Rahway, USA Mandatory Skills: AWS, Open lab CDS JD ReseClienth Infrastructure Product Line, Data Movement and Storage Produc…
1 month ago +

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.