Backend Developer, Data Engineer, ML Engineer
My single biggest interest is data analysis. Most of all I am fascinated by neural networks and their usage in different spheres like image recognition, text classification and of course financial markets.
During my first bachelor I have gained a sufficient theoretical background in mathematics and statistics which allows me to work in scientific contexts without difficulties. During my second bachelor I have gained a solid set of programming skills.
In my spare time I continue learning new things: online courses (from technical courses to neurobiology, cause it's cool), new languages and tech (block chain and solidity were my recent adventure). I take part in GameJams quite frequently. I do not forget about the body and soul: sport, meditation and all in all healthy life style is a must.
I am an easy going person able to work in a team as well as alone. Self-organization is not a foreign concept for me. In my daily life I use ToDo-lists and even Trello.
The bachelor thesis consisted in proving that classical portfolio models are ineffective on modern Russian stock exchanges. Thus I suggested an adopted approach for choosing the portfolio for a private investor.
The goal of my second bachelor thesis was to check what language is more suitable for an econometric task written on the beginner level. Python and R were chosen for comparison, since these two languages are heavily used by both scientific and non-scientific communities. To answer this question I have written two identical Programs (with respect to the language's features and idiomatic style) in Python and R and have run performace and memory usage tests. I have also compared subjective usability (ease of use, documentation quality, community) of the langages.
Working student, Graphics manipulation, design
Working student, front end developer (Angular.js, Bootstrap, JS)
Fulltime software developer (mostly back end development and text classification via ml and standard approaches. Java, Python, ES, SQL)
A 30 minutes presentation on the very basics of a short-text classification starting from 4:36:00