| Looking for R-SAS Programming in Remote at Remote, Remote, USA |
| Email: [email protected] |
| From: Khursheed, RHG [email protected] Reply to: [email protected] Hi All, Hope you all are doing great, Please go through the below job description and share your qualified consultants. R/SAS Programming Remote Key Responsibilities: Statistical Analysis and Visualization: Develop and maintain robust R scripts for the generation of tables, listings, and graphs (TLGs) to support clinical study reports, publications, and other regulatory submissions. Interpret statistical analysis plans and develop corresponding programming solutions. Code Conversion: Convert existing SAS code to R and vice versa, ensuring consistency and accuracy in results. Collaborate with SAS programmers to understand the intricacies of the original code and ensure a seamless transition. Shiny App Development: Design, develop, and maintain interactive Shiny applications to visualize and interpret clinical data. Collaborate with cross-functional teams to gather requirements and ensure the Shiny apps meet the needs of end-users. Python Integration: Utilize Python for specific data processing tasks, integration with other systems, or to complement R-based analyses. Ensure seamless interoperability between R and Python scripts. Documentation & Quality Control: Maintain thorough documentation of all programming activities to ensure reproducibility and adherence to internal standards.Key Responsibilities: Statistical Analysis and Visualization: Develop and maintain robust R scripts for the generation of tables, listings, and graphs (TLGs) to support clinical study reports, publications, and other regulatory submissions. Interpret statistical analysis plans and develop corresponding programming solutions. Code Conversion: Convert existing SAS code to R and vice versa, ensuring consistency and accuracy in results. Collaborate with SAS programmers to understand the intricacies of the original code and ensure a seamless transition. Shiny App Development: Design, develop, and maintain interactive Shiny applications to visualize and interpret clinical data. Collaborate with cross-functional teams to gather requirements and ensure the Shiny apps meet the needs of end-users. Python Integration: Utilize Python for specific data processing tasks, integration with other systems, or to complement R-based analyses. Ensure seamless interoperability between R and Python scripts. Documentation & Quality Control: Maintain thorough documentation of all programming activities to ensure reproducibility and adherence to internal standards.Key Responsibilities: Statistical Analysis and Visualization: Develop and maintain robust R scripts for the generation of tables, listings, and graphs (TLGs) to support clinical study reports, publications, and other regulatory submissions. Interpret statistical analysis plans and develop corresponding programming solutions. Code Conversion: Convert existing SAS code to R and vice versa, ensuring consistency and accuracy in results. Collaborate with SAS programmers to understand the intricacies of the original code and ensure a seamless transition. Shiny App Development: Design, develop, and maintain interactive Shiny applications to visualize and interpret clinical data. Collaborate with cross-functional teams to gather requirements and ensure the Shiny apps meet the needs of end-users. Python Integration: Utilize Python for specific data processing tasks, integration with other systems, or to complement R-based analyses. Ensure seamless interoperability between R and Python scripts. Documentation & Quality Control: Maintain thorough documentation of all programming activities to ensure reproducibility and adherence to internal standards.Key Responsibilities: Statistical Analysis and Visualization: Develop and maintain robust R scripts for the generation of tables, listings, and graphs (TLGs) to support clinical study reports, publications, and other regulatory submissions. Interpret statistical analysis plans and develop corresponding programming solutions. Code Conversion: Convert existing SAS code to R and vice versa, ensuring consistency and accuracy in results. Collaborate with SAS programmers to understand the intricacies of the original code and ensure a seamless transition. Shiny App Development: Design, develop, and maintain interactive Shiny applications to visualize and interpret clinical data. Collaborate with cross-functional teams to gather requirements and ensure the Shiny apps meet the needs of end-users. Python Integration: Utilize Python for specific data processing tasks, integration with other systems, or to complement R-based analyses. Ensure seamless interoperability between R and Python scripts. Documentation & Quality Control: Maintain thorough documentation of all programming activities to ensure reproducibility and adherence to internal standards. Best Regards, Khursheed Keywords: rlang golang |
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| Tue Sep 26 18:29:00 UTC 2023 |