Passionate Data Scientist of Applied Mathematics and Physics background with more than 4 years of experience in machine learning, data analysis and software engineering. Worked on various machine learning projects (Python, R), natural language processing (NLTK, TensorFlow, Word2Vec), Recommender Systems (Python), deep reinforcement learning (TensorFlow, Keras, Theano, Gym), quantitative research and time series analysis (Pandas, sklearn), computer vision (OCR, facial recognition, facial identification), as well as classical web-development projects as a back-end engineer and have conducted research in genomic data science and apprenticeship learning (for optimal control tasks). Provided initial research and developed architecture of reinforcement learning system as a microservice for loaded data pipeline (Spark, HDFS)
Utilized SQL databases such as PostgreSQL, MySQL, DB2 and NoSQL (Redis, Neo4J, ElasticSearch). Familiar with cloud services such as Heroku and AWS (S3). Used team collaboration, issue tracking and VCS tools such as Jira, Redmine, Git (github, gitlab), Bitbucket, Trello.
Strongly interested in deep learning and new trends in data science (genetic algorithms for deep learning, deep reinforcement learning, inverse reinforcement learning, robotics, autonomous vehicles) as well as new deep learning frameworks (pyTorch, SyntaxNet). Curious about HDFS, Spark, Rabbitmq and Big Data stack for engineering complex data pipelines, streaming solutions and distributed computing.