Scientist Bioinformatics - Leiden

Job description

This position offers an exciting opportunity to develop and deploy bioinformatics-driven solutions in a clinical-stage biotechnology company, where quality, speed, motivation, team spirit and results are of utmost importance to be successful.


As a member of the bioinformatics team, you will participate in bioinformatics efforts supporting the target and drug discovery projects at Galapagos. You carry out functional analyses and implement and execute data analysis methodologies. You work closely together with multidisciplinary disease biology and drug discovery teams to acquire scientific insights contributing to our research strategy. You collaborate with the Information Systems department to incorporate bioinformatics tools and methods into Galapagos’ informatics infrastructure.

Requirements

You have a Master Degree with at least 3 years of relevant work experience or a PhD with a demonstrated track record in applied bioinformatics preferably in a pharma/biotech setting. You are a team player with excellent communication and interpersonal skills, able to present data analysis results and explain bioinformatics methodologies clearly and convincingly to multidisciplinary teams. You are fluent in English and you are familiar with project-based working. 


You have in depth hands-on experience in various research and bioinformatics technologies including but not limited to:
·         Disease-related pathways and systems biology in a drug target discovery setting
·         Graph databases (e.g. NEO4J) and relational databases (e.g. Oracle, MySQL)
·         Expression data analysis, micro-arrays and/or sequencing-based, and experience in developing analysis strategies and workflows in transcriptomics
·         Working knowledge of Linux
·         Programming skills in at least Python (including Django) or Perl
·         Statistical computing in R, including the BioConductor libraries, reporting in R (sweave/knitr) and interactive web-interfaces (R/shiny)
·         Text mining in a drug and target discovery environment
·         Data-science techniques (regression, machine learning) and data-interchange formats (e.g. JSON)