Pavel has been active in the financial markets since 2007, initially as a retail trader, and later as a portfolio manager and an automated trading systems developer. His strategies have been deployed in managed portfolios exceeding $25 million.

Since primary school, he has devoted himself to numerical methods and machine learning. At the end of 2011, he began working with classical (shallow) neural networks. In 2013, he was among the first researchers in the Czech Republic to study deep neural networks (deep learning), before these methods evolved into the most promising area of artificial intelligence. He utilized deep convolutional neural networks to classify the spectra of space objects. In 2014, he defended his master's degree thesis at Masaryk University in Brno in the field of Theoretical Physics and Astrophysics. His work achieved breakthrough results, and after publication, it received positive international reviews and citations from other authors.

Pavel has continued to apply these methods in the financial markets. He has worked for leading asset management companies in the Central European region and significantly contributed to the development and optimization of option strategies on commodity ETFs and volatility for a top Central European hedge fund. One of his later projects involved creating deep architectures for market making systems on the major European equity markets for Wood & Company investment bank.

Currently, he manages SpreadCharts and acts as a machine learning consultant on the development of other interesting fintech projects around the world.