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R Developer with Life Science Background

Jobgether

🇬🇧 English
Shiny JavaScript Git reproducible pipelines databases

Descripcion del puesto

About the role

This position is for an R Developer with a life‑science background, based in Spain. The role sits at the intersection of software engineering, data science, and biomedical research, creating production‑grade R and Shiny applications that support drug discovery, clinical trials and other biomedical analytics.

Key responsibilities

  • Design, develop and maintain high‑quality R and Shiny applications for biological and clinical data analysis.
  • Process, analyse and visualise complex life‑science datasets, ensuring reproducibility and usability.
  • Build and maintain reusable R packages and data‑processing components for scalable analytics.
  • Collaborate with scientists, bioinformaticians and stakeholders to translate requirements into technical solutions.
  • Develop interactive visualisation tools to aid research and clinical decision‑making.
  • Use Git for version control and collaborative development.
  • Participate in system design discussions, backlog refinement and continuous improvement of development practices.
  • Support deployment and maintenance of production‑grade applications in cloud or distributed environments.

Required profile

  • Strong proficiency in R programming with hands‑on experience in production environments.
  • Experience working with biological, clinical or life‑science data.
  • Proven experience building web applications using R Shiny or JavaScript.
  • Experience creating, maintaining and documenting R packages.
  • Solid understanding of Git and collaborative software development workflows.
  • Master’s degree in Bioinformatics, Data Science, Computer Science, Biostatistics, Mathematics or related field, or equivalent experience.
  • Knowledge of molecular biology, genomics or clinical research is highly desirable.

Required skills

  • R programming
  • Shiny framework
  • JavaScript (optional for web UI)
  • Git version control
  • Cloud or distributed computing environments
  • Reproducible data pipelines
  • Database interaction

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