Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 2

•	Developed and implemented a working project for cyberpolice department as backelour graduation work;
•	Developed AI/ML decisions for medicine;
•	Working on many commercial projects as time-series analysis, best advertisment campaign prediction, etc.;
•	Had implemented distributed learning on two servers with GPU using the horovod framework
Worked with:
•	Time series processing: analysis and pattern extraction from time series. Clustering time series based on extracted patterns;
•	Image processing: image denoising and deblurring, image segmentation, image feature engineering using the Fourier analysis and Gauss transformations, image preprocessing. Clustering of segmented images by their feature vectors;
•	Retail: customer segmentation, selection the best marketing companies based on their impact on customer.
•	NLP: medical articles processing, extracting sentence embeddings, training multiclass-classifier of embeddings.
Technology stack: 
•	Programming languages: Python;
•	Frameworks, libraries: pandas, numpy, matplotlib, seaborn, yellowbrick, PIL, Tensorflow 2.0, Keras (CNN, GAN, AutoEncoders, DataGenerator, Optimizers), PyTorch, huggingface models, scipy (fft, affine_transform, gaussian_filter), XGBoost, LightGBM, scikit-learn (SVM, RandomForest, K-Means, DBSCAN, KFold, GridSearchCV), horovod (used for distributed learning);
•	Tracking and VCS: neptune.ai, git;
•	IDE’s: Jupyter notebook, Visual Studio Code, JetBrains PyCharm.
•	Databases: MSSQL
I’m familiar with math, probability theory and statistic. Also have experience with Java and C++.
The main criterion for me is the interest of the project. So I expect that the new cases at work will be non-standard, complex and extremely interesting.