Senior R Programmer/ Shiny Developer

Overview In this role you will design, build, and optimize interactive R Shiny applications that support clinical trial activities, enabling scalable data analysis and efficient regulatory reporting.You work within the Biostatistics team to deliver user-friendly tools for data exploration, quality control, and statistical review, while promoting reusable components and high-performance performance.You will help ensure compliance with GxP and CSA standards and contribute to open-source R/shiny initiatives.Responsabilidades Design, develop, test, and maintain interactive R Shiny apps for clinical trial data analysis Create user-friendly tools for statistical review, data exploration, QC, and reporting Develop reusable Shiny modules and frameworks for consistency and efficiency Optimize performance, scalability, and usability of applications Build utilities using R, SAS, and Python Support SDTM/ADaM review, dataset comparison, metadata review, TLF review and QC, data visualization, and statistical reporting Ensure applications comply with GxP, CSA, and company standards Plan and execute unit, integration, and regression testing Provide technical consultation on R programming and Shiny development Develop programs supporting project deliverables (ADaM, TLFs) and assist study teams with programming activities Requisitos principales 5+ years of programming experience in pharmaceutical/biotech/CRO environments 3+ years hands-on experience with R, R packages, RStudio, and RShiny in clinical data analysis Experience integrating Shiny apps with cloud data sources (AWS S3, Azure Data Lake) Ability to scale Shiny apps to hundreds of users and perform multi-level optimization Experience validating Shiny apps to meet FDA/EMA/GxP guidelines Proven track record building tools/pipelines that improve efficiency and standardization Active participation in open-source community related to R and Shiny Strong communication skills to translate technical strategy for technical and non-technical stakeholders Ability to work cross-functionally and influence product direction R, R packages, RStudio, and RShiny SAS Python Cloud data storage (AWS S3, Azure Data Lake)

You work within the Biostatistics team to deliver user-friendly tools for data exploration, quality control, and statistical review, while promoting reusable components and high-performance performance. You will help ensure compliance with GxP and CSA standards and contribute to open-source R/shiny initiatives. Responsabilidades Design, develop, test, and maintain interactive R Shiny apps for clinical trial data analysis Create user-friendly tools for statistical review, data exploration, QC, and reporting Develop reusable Shiny modules and frameworks for consistency and efficiency Optimize performance, scalability, and usability of applications Build utilities using R, SAS, and Python Support SDTM/ADaM review, dataset comparison, metadata review, TLF review and QC, data visualization, and statistical reporting Ensure applications comply with GxP, CSA, and company standards Plan and execute unit, integration, and regression testing Provide technical consultation on R programming and Shiny development Develop programs supporting project deliverables (ADaM, TLFs) and assist study teams with programming activities Requisitos principales 5+ years of programming experience in pharmaceutical/biotech/CRO environments 3+ years hands-on experience with R, R packages, RStudio, and RShiny in clinical data analysis Experience integrating Shiny apps with cloud data sources (AWS S3, Azure Data Lake) Ability to scale Shiny apps to hundreds of users and perform multi-level optimization Experience validating Shiny apps to meet FDA/EMA/GxP guidelines Proven track record building tools/pipelines that improve efficiency and standardization Active participation in open-source community related to R and Shiny Strong communication skills to translate technical strategy for technical and non-technical stakeholders Ability to work cross-functionally and influence product direction R, R packages, RStudio, and RShiny SAS Python Cloud data storage (AWS S3, Azure Data Lake)