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

I helped the company improve the main process (make the process faster, flexible and automatical), reach a business goal and save money and time)
work experience
Key responsibilities: quick prototypes and hypothesis validation, training models: supervised and unsupervised learning, both classical and DL approaches; data preprocessing, interactive tools for the analytics team, data visualization, fixing imbalanced data, code optimization. 
Tech stack: Python for Data Science (pandas, numpy, scipy, scikit learn, matplotlib, seaborn, xgboost, catboost, joblib), Deep Learning (Keras, TensorFlow), Python for ETL (Dask, hdfs3, Clickhouse), Jupyter Notebook (ipywidgets), SQL, Git, SSH
 
projects at university
Together with some university courses taken, I have been learning online about Data Science and Machine Learning. My framework is Python with pandas, numpy, scikit-learn, matplotlib and seaborn. For deep learning, I am using Keras + TensorFlow. 
I have made a personal and team projects for university courses(Data Analytics and Data Mining,  Network Optimization, Parallel computing, Machine Learning), as well as solutions for kaggle competitions(Sberbank Moscow Housing Prices, )
I enjoy the process of  data-driven decision making. It combines my technical math education with my passion to design. I like designing clear and self-explanatory visualizations with wide range of tools - matplotlib, seaborn, pyplot, folium - and provide strong statistical evidences.
I am pretty flexible and don't have criteria for the company, but good managing in a team and corporate culture is a good plus.