Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 1.5

I've worked at every stage of DS developmet pipeline: from data collection (statement of requirements, development of domain specific markup tool) to model development and deployment.
I've also won some money prizes in several kaggle-in-class hackathons.
I have experience in development of recommender systems. I've also worked with NLP tasks such as: text classification, extractive and abstractive text summarization and named-entity recognition. 
Furthermore, I am keen on different kaggle/kaggle-in-class hackathons, so I have successful experience in solving some credit scoring, recommender systems, text classification and some other problems.
My key hard skills:
>Python DS stack: jupyter, pandas, numpy, scikit-learn, bs4, spacy, nltk, gensim
>Deep Learning frameworks: pytorch, chainer, keras, tensorflow
>Boosting: xgboost, lightgbm, catboost
>Databases: PostgreSQL, MongoDB, CouchDB, Redis
>BE stack: Flask, Docker
>Strong math background: probability theory, statistics, regression analysis, functional analysis, optimization.
I would like to work in a small open-minded team on an interesting R&D project with new technologies.