Position: Data Scientist
Category: Data Science
English level: intermediate
Years of experience: 1

Internship Student - DataRoot University • Kyiv 
2017-10 - 2018-04 
Intensive Data Science trainee Program where you learn the theory needed for any full-cycle data science project development:

- Python for Data Science (math & libs, python practice. tableau)

- Machine Learning (supervised learning, unsupervised learning, scikit-learn)

- Deep Learning (deep neural networks, computer vision (CNN), sequence models (RNN), keras, tensorflow)

- Big Data & Data Storages (data mining algorithms, data storages, visualization tools)
To consolidate the covered material, I took part in the Kaggle competition, where according to results I entered in the top 100.


Sentiment Analysis of Social Media in Russian • Kiev 
2018-10 - 2018-11 
The task was to build a multi-headed a model that’s capable of detecting different types of sentiments in Russian VKontakte posts. For this task RuSentiment dataset was used. RuSentiment is currently the largest in its class for Russian, it contains 30,521 posts and wich are divided into 5 classes. 
To solve this task I use pre-trained vectors for russian language trained on Common Crawl and Wikipedia using fastText. 
The best performance was RocAUC score of 0.88 in 5-class classification on the test set. It was achieved by a CNN classifier with in-domain FastText embeddings. 
Technologies used: CNN, RNN, Logistic Regretion, fastText, Tf-idf, Word2vec

Recognizing text from a document photo
2019-05 - 2019-09
The task was to create a chatbot that could recognize certain text fields in a document from a photograph. As a result, my program was able to correctly recognize more than 90% of the documents from the test data set. This gave a huge leap in productivity and saved a lot of time and money for the company in which I worked.
Technologies used: CNN, OpenCV OCR, Tesseract, Google Cloud, Heroku, Telegram bot API
- Experience with Python (Pandas, Numpy, Scikit-learn, Scipy, Seaborn, Matplotlib)
- Math (statistics, the theory of probability, linear algebra)
- Machine Learning algorithms (Decision trees, Naive Bayes Classifier, Random forest, SVM, KNN, PCA, K-means, neural networks, regression, clustering etc)
- Deep Learning(CNN, RNN, TensorFlow, Keras,  CNN, RNN)
- Experience with version control systems (Git)
- MySQL, PostgreSQL
- Docker
Part-time of full time job with flexible working hours. Professional growth. Office near the metro station. Complex tasks. Small team. Ability to work remotely.