Senior Computational Biologist

Merck Merck · Pharma · North Brabant, Netherlands

Senior Computational Biologist role focused on applying computational biology and data science, including AI protein methods, to animal health challenges in manufacturing, diagnostics, and R&D. The role involves leading projects, developing software code for data science algorithms, and evaluating models using R or Python, with a focus on embedding analytics into business processes.

What you'd actually do

  1. Formulate and lead computational biology projects, including defining and executing appropriate benchmarks and _in silico _experiments
  2. Own project delivery and quality assurance
  3. Select appropriate algorithms, infrastructure, and benchmark to best solve challenges
  4. Drive the development of high-quality, well-documented, and reusable software code to execute data science algorithms
  5. Communicate and collaborate with diverse stakeholders

Skills

Required

  • PhD in Computer Science, Biomedicine, Molecular Biology, or related field. Alternative, MSc with at least 6 years of R&D experience.
  • Deep understanding of biological processes
  • Expert-level proficiency in R and/or Python
  • Experience with version control systems such as Git and collaboration tools
  • Strong analytical and problem-solving skills

Nice to have

  • Familiarity with cutting-edge AI protein methods
  • Extensive knowledge in one or more of the following areas: wet lab techniques, bioinformatics, biology, structural biology, or machine learning
  • Track record of delivering successful data science products
  • Experience with cloud computing or HPC
  • Demonstrated communication and interpersonal skills, including the ability to deliver high-level presentations to senior executives
  • Ability to work both independently and collaboratively within a globally dispersed team

What the JD emphasized

  • cutting-edge AI protein methods

Other signals

  • applying computational biology and data science to solve real-world challenges
  • drive the development of high-quality, well-documented, and reusable software code to execute data science algorithms
  • Use R or Python to develop, implement, benchmark, and evaluate models
  • Familiarity with cutting-edge AI protein methods