Position: Data Scientist
Category: Python
English level: upper
Years of experience: 1


First, I worked in the University Lab (Taras Shevchenko National University of Kyiv, Nuclear Spectroscopy laboratory) as a senior lab assistant. I was working on analyzing the dataflow taken from the Lumical calorimeter system at DUNE neutrino experiment. I used ROOT data analysis framework (C++) and Python data analysis libraries: numpy, matplotlib, scipy, pandas, and iminuit. The working process included data processing, data selection, statistical analysis, physics constraints optimization, and fitting. At the same time, I was working on my bachelor's thesis, which covers an analysis of rare physical processes at Belle II experiment (Japan). This job contained a lot of mathematical approaches such as Monte-Carlo simulations, using basf2 framework (python 3) and physical events' reconstruction. 
After successfully finishing the bachelor's program at TSNUK and enrolling in a master's degree at the same University I started to work on the Belle analysis. This work includes writing a Monte-Carlo code (Evtgen framework) for physics process event simulation and further processing them (reconstruction) in the frame of BASF (Belle Analysis Software) program. I have written a difficult object-oriented code using BASF libraries in C++ and used some well-known algorithms such as the depth-first search for increasing the performance of the program. All these steps also include writing shell scripts (mostly bash, sometimes csh) for automatizing and remote controlling the above-mentioned tasks. I've been working with Linux (Ubuntu, Mint, CentOS, Scientific Linux) for about 4 years, and in addition, also have a bit of background in crypto-oriented projects like testnets where I also got solid Linux experience. 
In a few months, I started my remote internship at the European Organization for Nuclear Research (CERN) where I work on performance studies and software profiling of FCCAnalyses framework proposed for solving data analysis problems in the prospective Future Circular Collider experiment. The framework is written in C++ under the hood, built on the functional programming paradigm, but uses a Python API for users to set parameters, configuration, and other physics properties to analyze. I had to use the profiling tools that allow CPU profiling of both JIT-ed, multithreaded C++, and Python codes. I worked with perf, cProfile, callgrind, gprof, and gperftools. In the meantime, I also worked in DESY on a data-analysis project in the frame of CMS experiment (C++).