Pavel has been active in the financial markets since 2007. First, as a retail trader, and later he became a portfolio manager and an automated trading systems developer. His strategies have been deployed on managed portfolios of more than $10 million.

Since primary school, he has devoted himself to numerical methods and machine learning. At the end of 2011, he started to deal with classical (shallow) neural networks. Then in 2013, he was one of the first researchers in the Czech Republic to study deep neural networks (deep learning). In fact, this was before these methods developed into the most promising area of artificial intelligence. He dealt with the use of 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. In his work, he has achieved breakthrough results. After his thesis was published, it received positive reviews abroad and has been cited by other authors.

Pavel has continued to apply these methods in the financial markets. He worked for leading asset management companies in the Central European region. He contributed significantly to the development and optimization of option strategies on commodity ETFs and volatility for the top Central European hedge fund Charles Bridge Global Macro Fund. One of his latest projects is, for example, the work on creating deep architecture for market making systems on the major European equity markets for the Wood & Company investment bank.

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