Raphael Peer

Raphael Peer has a diverse work experience in various roles and industries. Raphael started their career as an intern at Max Perutz Labs Vienna, where they conducted literature research on cognate RNA-protein binding. Raphael then moved on to CUBE - Computational Systems Biology at the University of Vienna, where they completed an internship and subsequent part-time work in the computational systems biology group, focusing on modifying and using MySQL fulltext-search for protein sequence similarity-search.

In 2014, Raphael joined the MRC Laboratory of Molecular Biology (LMB) as a Masters Project, where they worked on their Master's thesis in computational structural biology, analyzing residue interaction networks of small G proteins.

In 2017, Raphael began their role as a Data Scientist at Blumatix Consulting GmbH, where they worked until 2020. During this time, they gained valuable experience in data science.

In 2020, Raphael joined Klarna as a Data Scientist, and later became a Senior Data Scientist. Raphael worked in this role until 2023, honing their skills in data analysis and modeling.

Currently, Raphael is working as a Machine Learning Engineer at Inscribe, where they started in July 2023. This role allows him to apply their expertise in machine learning to develop innovative solutions.

Throughout their career, Raphael has worked on various contract projects with companies like Porsche Informatik and Siemens Vienna, further expanding their knowledge and skills.

Overall, Raphael Peer has a strong background in data science, computational biology, and machine learning, making him a valuable asset in the field.

Raphael Peer completed their Bachelor of Science in Biology/Molecular Biology at the University of Vienna from 2009 to 2013. Raphael then pursued further education at the same institution and obtained their Master of Science in Molecular Biology from 2013 to 2016. Afterward, Raphael attended Paris Lodron Universität Salzburg from 2016 to 2021, where they completed their Master of Science in Data Science. In addition to their formal education, they also earned certifications in Machine Learning and Practical Machine Learning from Coursera in September 2018 and December 2017, respectively.