Position: Data Scientist / Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 5

Google Tensorflow Certified data scientist with 5+ years of experience
5 peer-reviewed publications
Two times finalist at International Data Analysis Olympiads, obtaining 16th place among 2187 teams.
Tensorflow Certified data scientist with 5 peer-reviewed publications and proven practical experience in machine learning, natural language processing, predictive modeling, and python. Two times finalist at International Data Analysis Olympiads, obtaining 16th place among 2187 teams.Looking to use my experience to build machine learning and data-related solutions.

Senior Data Scientist / Machine Learning Engineer
( 5 years )
-Generated word embeddings and applied them to sentiment analysis. Handled data collection, parsing, text preprocessing, model building, and model evaluation steps. Obtained 85% accuracy score on extrinsic evaluation task. Models: word2vec, CBOW, skip-gram, GLoVe. Stack: gensim, Tensorflow, Keras, Matplotlib, seaborn, NLTK, spaCy, fastText.
-Built a machine learning model to predict customer waiting time in the queue leading to a reduction of 26% in queue-related complaints. Stack: Pandas, NumPy, XGBoost, CatBoost, LightGBM, Docker, matplotlib.
-Wrote language-specific text analyzers in Elasticsearch. Improving the search functionality of our websites. Stack: Elasticsearch, Pandas, SQL, Tableau. 
-Developed sentiment analysis model for measuring user satisfaction which reduced 12 hours per week of manual labeling and analysis time. Models: SVM, LSTM, RNN, BERT. Stack: Tensorflow, Keras, scikit-learn, SQL, Tableau, Pandas.
-Trained an RNN for generating text based on the works of outstanding poets.
Trained custom named entity recognition system. Stack: LSTM, sklearn-crfsuite, spaCy, NLTK, eli5.
-Worked on the text classification and automated analysis of news articles which involved training machine learning algorithms for classification and analysis. Built data preprocessing, vectorization, and feature engineering pipelines. Trained various machine learning / deep learning models for text classification and keyword extraction. Implemented probability calibration and feature reduction for performance improvements. Stack: MongoDB, Tensorflow, Keras, scikit-learn, SQL, Tableau, Pandas. Fast-API.
Responsible for the full machine learning lifecycle: predictive models, development, new ideas inducing, proof of concepts, implementation in production environment, monitoring.
I am a team player. Therefore I love big projects and well-established processes.