Soft-, ML-, Simulation Engineer and Data Scientist
There is no such thing as a perfect tool to build a robust system. Rather, there is a tool (or tools) that is the most suitable for each particular job. And that’s what I do: I make sure that the best possible stack to solve this particular problem is used. That the codebase is clear, simple and maintainable. That documentation is present AND USEFUL!
I constantly learn new things at work and in my free time: I like combining new and time-tested approaches to solve a problem I’m working on. I participate in conferences and workshops, sometimes as a listener and sometimes as a presenter: I enjoy giving back to the community that taught me so many useful skills for free.
AMLD2019: Data analysis workshop organizer
Software and Infrastructure consultant
Senior Machine Learning Engineer (Bakend, Data pipelines, BI and Reporting, ML/DS)
Machine Learning Engineer (Data preparation; streaming; chron jobs; backend engineering; highly specific, high performance simulation framework)
Fulltime software developer (Data Engineering, Backend Development, NLP)
Working student, front end developer (Angular.js, Bootstrap, JS)
Working student, Graphics manipulation, design
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.