• Designs, develops, tests, and maintains interactive R Shiny applications that support clinical trial activities, ensuring they are scalable and performant for large-scale clinical data analysis.
• Creates user-friendly applications for statistical review, data exploration, quality control, and reporting.
• Develops reusable Shiny modules and application frameworks to promote consistency and efficiency.
• Optimizes application performance, scalability, and usability.
• Develops reusable programming utilities using R, SAS, and Python.
• Builds applications that support: SDTM and ADaM review; Dataset comparison; Metadata review; TLF review and quality control; Clinical data visualization, Statistical reporting.
• Develop applications in accordance with GxP, Computer Software Assurance (CSA), and company standards.
• Develops and executes application testing, including unit, integration, and regression testing.
• Provides technical consultation on R programming, R Shiny development, and open-source technologies.
• Develop programs to support project deliverables (i.e. ADaM, TLFs, etc)
• Supports study teams with programming activities on projects.

What we are searching for:

• ​Bachelor's degree in Statistics, Computer Science, Mathematics, Data Science, or a related quantitative field (Master's preferred). 
• 5+ years of programming experience in the pharmaceutical, biotechnology or CRO industry 
• 3+ years of hands-on experience with R, R packages, RStudio, and RShiny, particularly in the context of clinical data analysis. 
• Experience in integrating RShiny applications with various data sources, particularly cloud-based data storage solutions like AWS S3, Azure Data Lake, or similar platforms. 
• Ability to scale Shiny applications to hundreds of users, with experience in performance optimization at various levels (frontend, backend, infrastructure). 
• Experience in validating RShiny applications to meet regulatory standards such as FDA, EMA, and GxP guidelines. 
• Demonstrated experience building tools, macros, or pipelines that improved efficiency or standardization for a programming function. 
• Active participation in the open-source community, particularly in developing and maintaining R packages and Shiny applications.
• Expertise in optimizing the performance of Shiny applications, including frontend, backend, and infrastructure levels, to ensure they can handle large datasets and multiple users efficiently. 
• Ability to analyze user behavior and interactions with Shiny applications to improve usability and functionality. 
• Strong communication skills, with the ability to translate technical strategy for both technical and non-technical stakeholders.