Federico Comoglio
Head Of Center Of Competency at PartnerRe
Bern, Bern, Switzerland
Summary
👤
Senior🎓
Top SchoolFederico Comoglio is a computational problem-solver and technology leader with 12 years of experience bridging genomics, biomedical data science, and actuarial analytics. As Head of Center of Competency at PartnerRe and COO/Chief Scientist at enGene Statistics, he designs and ships data products and cloud solutions that automate complex actuarial and clinical workflows while meeting strict regulatory constraints. His background spans academic-scale bioinformatics and ML—from scalable (epi)genomics pipelines and interpretable multi‑omics models to CE‑marked medical device algorithms for sleep apnea—bringing rigorous reproducibility and engineering practices to production. He has a PhD from ETH Zurich and a track record of securing competitive research funding and mentoring teams across Europe. Notably, he established a cross-functional generative AI initiative at a global reinsurer, applying cutting‑edge models to Life & Health business problems. Based in Bern, he combines scientific curiosity with product-focused delivery, turning high-dimensional biological insight into practical, auditable solutions.
12 years of coding experience
9 years of employment as a software developer
High School Diploma, Computer Science, 100/100, High School Diploma, Computer Science, 100/100 at ITIS Q. SellaDr. sc. (PhD), Epigenomics | Computational biology, Dr. sc. (PhD), Epigenomics | Computational biology at ETH ZurichM.Sc., Pharmaceutical Biotechnology, Summa cum Laude, M.Sc., Pharmaceutical Biotechnology, Summa cum Laude at Università degli Studi del Piemonte Orientale
Italian, German, English
Github Skills (62)
stock10
trading9
r-package9
finance9
seq9
fintech9
angular9
rna-seq9
absolute8
genomics8
organize8
nestjs8
prisma8
typescript8
javascript8
Programming languages (6)
Github contributions (5)
FedericoComoglio/Rknots
Sep 2013 - Nov 2018
Contributions:20 commits, 12 pushes in 5 years 2 months
FedericoComoglio/dupiR
Sep 2013 - May 2021
An R package for absolute quantification under uncertainty. dupiR implements a Bayesian approach using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement.
Contributions:79 commits, 10 PRs, 65 pushes in 7 years 9 months
r-packageabsoluteapproachconjugatediscrete
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