Thorsten Tiede is a Senior Scientist and Data Engineer with a decade of experience bridging bioinformatics research and production data engineering for personalized medicine and drug discovery. Trained as a bioinformatician (Dr. rer. nat.) at the University of Tübingen, he has led large-scale omics analytics, built reproducible service-oriented platforms with provenance-tracking, and implemented graph-backed RESTful systems using Java Spring and Neo4j. He combines early-career expertise in Oracle-based data warehousing and BI with recent hands-on work building cloud-native lab-to-cloud pipelines and web frontends for assay data. Thorsten has taught undergraduate and graduate bioinformatics and cheminformatics courses, co-authored a book chapter on biomedical data management, and presented at international conferences. He recently deepened his ML/Deep Learning skills across scikit-learn, TensorFlow, PyTorch and the PyData stack, bringing research-grade models closer to deployable decision support. Based in Basel and currently at Roche pRED, he is known for making complex biological data auditable and actionable for scientists and clinicians.
10 years of coding experience
16 years of employment as a software developer
Dr. rer. nat., Applied Bioinformatics, Dr. rer. nat., Applied Bioinformatics at University of Tübingen
Contributions:8 PRs, 22 pushes, 2 branches in 1 year 9 months
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