Maarten Kooyman is a data scientist with 14 years of hands-on experience applying bioinformatics and high‑throughput computing to human genetics and large-scale sequencing projects. He blends software engineering (C++, Python, Perl, JavaScript) and DevOps skills (Jenkins, Puppet, Bioconda packaging) to make complex pipelines reproducible and deployable across clusters and HPC clouds. His background as a technical consultant and datamanager means he routinely supports researchers end-to-end—from cluster maintenance and workflow automation to exome analysis for international collaborations. A long-time bioinformatician, he contributes to the prominent bioconda-recipes ecosystem, writing wrappers and build scripts that help distribute NGS tools reliably. Based in the Randstad, he pairs domain expertise in GATK, Bioconductor and Ensembl with practical systems know-how, quietly enabling research at scale.
14 years of coding experience
2 years of employment as a software developer
Bachelor of Science (BSc), Medical Natural Sciences, Bachelor of Science (BSc), Medical Natural Sciences at Vrije Universiteit Amsterdam
Master of Science (MSc), Genetic Epidemiology, Master of Science (MSc), Genetic Epidemiology at NIHES
Master of Science (MSc), Bioinformatics, Master of Science (MSc), Bioinformatics at University of Nijmegen
Propedeuse Technical Physics/ Engineering, Physics , Enigneering, Propedeuse Technical Physics/ Engineering, Physics , Enigneering at Rotterdam University of Applied Sciences
Contributions:9 reviews, 16 commits, 19 PRs in 3 years 10 months
Contributions summary:Maarten contributed to the bioconda-recipes repository by creating and updating recipes for bioinformatics tools. Their work involved writing Python wrapper scripts for Java-based applications, modifying build scripts (bash), and integrating new packages. They also demonstrated skills in package management, build automation, and ensuring the correct execution environment for software packages. Their work focused on ensuring software compatibility and efficient deployment within the Bioconda ecosystem.
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