Python/R programmer (Contract) Full Time

Summary of Position:

We are seeking an experienced R Programmer with strong pharmaceutical industry experience and a solid understanding of clinical trial data. The ideal candidate will have expertise in R programming, data visualization, and clinical data analysis, with working knowledge of Python and AI tools considered a plus.

Position Responsibilities:

• Analyze and interpret clinical trial datasets (i.e. CDISC SDTM / ADaM)
• Development and QC of tables, listings, graphs
• Develop and maintain advanced data visualizations using industry-leading tools such as R Shiny, Python, and other relevant platforms.
• Support statistical programming, reporting, and exploratory analyses
• Collaborate with biostatistics, clinical, and data management teams to ensure all deliverables are accurate, consistent, and aligned with clinical and regulatory standards, enhancing accessibility and clarity of clinical trial data.
• Utilize R/Python and AI agent tools where appropriate to improve efficiency and automation

Candidate Requirements:

• Education: Bachelor or Master degree in Data Science, Statistics, Bioinformatics, Computer Science, or a related field. Advanced degree preferred.
• Experience:

Previous experience in a biotech, pharmaceutical, or clinical research environment.

3+ years of experience in data visualization, with a strong focus on clinical or biomedical data.

• Skills:

Familiarity with regulatory data standards (e.g., CDISC), clinical trial reporting guidelines, and industry best practices.

Strong understanding of clinical trial data structures (e.g., SDTM, ADaM) and clinical trial design.

Expert in data visualization tools such as R/R Shiny, Python, or similar software. Experience with AI tool is a plus.

Ability to translate complex statistical and clinical data into clear, accessible visual formats.

Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.

Strong problem-solving skills and a passion for continuous learning and improvement in data visualization techniques.

Attention to detail and a commitment to accuracy in clinical trial data visualizations.