Data Scientist, Software Engineer
I achieve business goals and projects deadlines by effectively utilizing skills from different areas: data analysis, machine learning, backe engineering and devops.
My current passion is building realistic synthetic data at scale which includes deep understanding of data and high performance computing. Automation and testing also play a crucial role in this direction ensuring high quality and reproducibility of the result.
Outside my day job, I like working on my own projects, collecting new skills in different areas (not necessary only in tech).
AMLD2019: Data analysis workshop organizer
Working student, Graphics manipulation, design
Working student, front end developer (Angular.js, Bootstrap, JS)
Fulltime software developer (Data Engineering, Backend Development, NLP)
Data Science Engineer (Data analysis, ML, Backend Development, Processes Automation, Simulation development)
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.