Summary
Daniel Banos is a Senior Data Scientist based in Switzerland with eight years of experience at the intersection of machine learning, computational biology, and environmental modelling. He has led applied ML projects at the Swiss Data Science Center—ranging from Europe-wide air pollution prediction to a mouse lipid brain atlas—and previously advanced closed-loop experimental design and Bayesian methods in systems biology and genetics during multiple postdoctoral roles. Trained with a PhD in bioinformatics from the University of Edinburgh and an MS in computer science, he blends rigorous probabilistic modelling and sparse-regression techniques with practical deployment for real-world scientific problems. Daniel’s background spans academia and applied research, giving him a track record of turning complex biological hypotheses into reproducible ML workflows and publications. A less obvious strength is his end-to-end fluency: he moves from UNIX system administration and teaching to cutting-edge Bayesian structure learning, enabling both hands-on experimentation and methodological innovation.
8 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (PhD), Biomathematics, Bioinformatics, and Computational Biology, Doctor of Philosophy (PhD), Biomathematics, Bioinformatics, and Computational Biology at The University of Edinburgh
Universidad Nacional Autónoma de México (UNAM)
Master of Science (MS), Computer Science, Master of Science (MS), Computer Science at Institute of Applied Mathematics and Systems, UNAM
English, Portuguese, Spanish, French