- Design, build, and deploy advanced RShiny applications on cloud platforms, ensuring they are scalable and performant for large-scale clinical data analysis.
- Collaborate with data scientists, biostatisticians, and research teams to create sophisticated RShiny applications that drive our clinical data analysis efforts.
- Utilize tools like Posit Workbench, Posit Connect, and AWS services to create workflows that support the deployment of R-based analytics applications.
- Develop and maintain robust data pipelines to pull, process, and visualize large datasets from data sources (Databricks) within RShiny applications.
- Work closely with data engineers and architects to ensure the seamless integration of data sources (Databricks) and tools used within the organization.
- Create and maintain technical documentation to support ongoing development and deployment processes.
- Minimum of 5 years of experience in the IT industry, with a strong emphasis on data visualization and dashboard development, particularly using R.
- Proven experience in the Life Sciences, Biotech, or Pharma industry is a must.
- Bachelor’s degree in computer science, Engineering, Information Systems, Data Science, or a related field is required. A master’s degree is preferred.
- 3+ years of hands-on experience with R, R packages, RStudio, and RShiny, particularly in the context of clinical data analysis.
- 2+ years of experience in building and maintaining CI/CD tooling (e.g., Gitlab, GitHub).
- Basic knowledge of SQL for querying databases and manipulating data within both relational databases and big data environments.
- Experience in integrating RShiny applications with various data sources, particularly cloud-based data storage solutions like AWS S3, Azure Data Lake, or similar platforms
- Advanced Shiny Techniques: Proficiency in using shiny modules, JS events, and custom bindings to extend the functionality of Shiny applications.
- User-Centric Design: Strong ability to implement visually appealing and user-friendly applications using HTML/CSS.
- Ability to scale Shiny applications to hundreds of users, with experience in performance optimization at various levels (frontend, backend, infrastructure).

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Other Key Skills
- Validation and Compliance: Experience in validating RShiny applications to meet regulatory standards such as FDA, EMA, and GxP guidelines. This ensures that the applications are reliable, secure, and compliant with industry regulations.
- Open Source Contributions: Active participation in the open-source community, particularly in developing and maintaining R packages and Shiny applications. This demonstrates a commitment to continuous learning and staying updated with the latest advancements in the field.
- Performance Optimization: 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.
- User Behavior Analysis: Ability to analyze user behavior and interactions with Shiny applications to improve usability and functionality. This involves understanding the diverse needs of users ranging from data scientists to clinicians.
- Data Integration: Proficiency in integrating Shiny applications with various data sources, including cloud-based storage solutions like AWS S3, Azure Data Lake, and other platforms.