Position: Data Scientist / Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 2

Conference Open Readings March 2019, Vilnium Lithuania: 
"The contribution is recognized as one of the best presentations on Life and Natural Sciences in 62nd International Conferences for Students of Physics and Natural Sciences "Open Readings 2019" "
--- Statement ---
Looking for a position of Middle Data Scientist/Machine Learning Engineer. Interested in Computer Vision, but in general would be glad to work with any type of data and tasks.

--- Responsibilities & Tasks ---
[Data Scientist 2 years]
R&D department of Sierentz Global Merchants
- Research, testing, assessment and development of machine learning solutions for geospatial clustering (Birch algorithm), predictive crop progress modeling (1D CNN) and predictive yield modeling (ensembles of RandomForest)
- Creation of machine learning systems infrastructure from scratch
- Evaluation and validation of new and existing models
- Exploratory data analysis & mining of geospatial and time series data (e.g. remote sensing data, crop progress data)
- Data scrapping, preprocessing, validation and visualization
- Feature engineering and dimensionality reduction 
- Development and code implementation of algorithms for extraction of extra information from time series data
- Report creation and presentation
- Management of Junior Data Scientists & Junior Machine Learning Engineers 

Also previously
[Research Assistant 3.5 years]
National Taras Shevchenko University of Kyiv
- Design, conduction and management of neuroscientific research projects
- Data collection, preprocessing and analysis
- Development and testing of research hypotheses
- Literature search, analysis & interpretation of research results
- Creation of reports and presentation of research results (poster & oral presentations)

--- Stack Overview ---
Main domain:
Machine Learning, Data Science, Probability & Statistics, Statistical data analysis 

Aux domain: 
Biology, Neuroscience, Research design 

Main stack:
Python, Git, Linux, Bash, PyTorch, TensorFlow, sklearn, numpy, pandas (+ geopandas), matplotlib, scipy, xarray, Open Data Cube, OpenCV

A bit familiar with: 
C, TFX, GoogleCloud, FastAPI

--- Side projects ---
- Basic solution for Image Colorization implemented in PyTorch 
Architecture: modified U-Net (ReLU changed to Leaky ReLU)
Datasets: CIFAR-10 + Dogs vs Cats