Advanced R programming skills and documented experience with package/library development in line with best practices
Familiarity with (general) good programming practices: unit testing, technical documentation, code encapsulation
Experience with version control/git
Familiarity with Unix command line (advanced bash scripting skills would be a plus),
Good cross-technical teams communication skills to ensure the right solution is being developed looking from overall system perspective
Ability to troubleshoot complex technical issues. willingness to constantly learn cuffing edge technologies and master new engineering skills.
Good communication skills, advanced English reading, writing, listening, speaking skills. Ability to communicate effectively with scientists (biologists / computational biologists), engineers and non-technical people.
Good understanding of biological concepts and biostatistical approaches commonly used in molecular biology and interest to learn more
Experience with NGS/genomic data analysis (GWAS, RNASeq, scRNASeq. ATAC-Seq etc.) and tools used for data processing and analysis, including solid understanding of underlying methods and algorithms.
Experience with the usage of Bioconductor packages and familiarity with data structures commonly used in the Bioconductor package ecosystem
Experience with data wrangling, pre-processing. visualization and handling of large datasets.
Hands-on experience with Shiny framework
(alternatively: any reactive JS framework),
Practical knowledge of data visualization techniques (familiarity with ggplot2 or Plotly is a plus)
Experience with web applications development to create usable and responsive frontend solution
Documented experience in building R Shiny production applications, (maintainable solutions using R/Shiny) Being able to understand solution architecture and role of involved software components to implement R Shiny app in a way that system does its job effectively and in a scalable way
- Offered Salary:
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