Grigoriy Volovskiy

Grigoriy Volovskiy

Chapter Lead Machine Learning @ Taxy.io

About Grigoriy Volovskiy

Grigoriy Volovskiy serves as the Chapter Lead for Machine Learning at Taxy.io in Köln, Germany, where he has worked since 2021. He holds a PhD in Statistics and has extensive experience in data science and academia.

Work at Taxy.io

Grigoriy Volovskiy has been serving as Chapter Lead Machine Learning at Taxy.io since 2021. In this role, he oversees machine learning initiatives and leads a team focused on developing innovative solutions in the field. Prior to this position, he worked as a Senior Data Scientist at Taxy.io starting in 2020. His experience at Taxy.io spans a total of four years, contributing to the company's advancements in data science and machine learning applications.

Education and Expertise

Grigoriy Volovskiy holds a Dr. rer. nat. (PhD) in Statistics from RWTH Aachen University, where he studied from 2015 to 2018 and graduated summa cum laude. He also earned a Bachelor of Science in Mathematics from RWTH Aachen University from 2008 to 2011, followed by a Master of Science in Mathematics from 2011 to 2013. Additionally, he completed a Master of Research in Mathematical Sciences at Imperial College London from 2013 to 2014. His academic background provides a strong foundation in quantitative analysis and statistical methodologies.

Previous Experience

Before joining Taxy.io, Grigoriy Volovskiy worked at RWTH Aachen University as a Statistical Research Specialist from 2015 to 2021. In this role, he focused on statistical analysis and research projects. He also served as a Graduate Teaching Assistant at Imperial College London from 2013 to 2015, where he supported the teaching of mathematical concepts and assisted students in their learning.

International Experience

Grigoriy Volovskiy participated in an Erasmus stay abroad at Imperial College London from 2012 to 2013. This experience allowed him to broaden his academic and cultural horizons while studying in a prestigious institution. His time in London contributed to his expertise in mathematical finance and enhanced his research capabilities.

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