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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.

Work experience

Sep 2018Current

Quantitative Researcher / Data Science Engineer

Well ATS

Researched into different trading strategies on cryptocurrencies market using statistical data analysis and numeric optimization on time series. Researched into money-management and risk-management approaches and their applications to portfolio of trading strategies.

Tech stack: Python, Pandas, sklearn, Tensorflow.


Nov 2017Aug 2018

Lead Data Scientist

T-shaped Crew

Kite-balancing project (tech lead). Technical management of 2 researchers.

  • Did initial research and development in algorithms  behind kite-balancing
  • Developed proof-of concept on application of GANs to sensory data augmentation (Keras, Tensorflow)
  • Developed behavioral-cloning framework to learn kite balancing from demonstrations (of an operator), using deep learning (Keras, Tensorflow)
  • Developed reinforcement-learning software to improve results of behavioral-cloning deep neural network using REINFORCE algorithm (and additional interactions with environment)
  • Developed integration of AI solution into embedded system (controller programmable with Arduino-C language)

Tech stack: Python, Tensorflow, Keras, keras-rl, reinforcement-learning, gym

Mar 2017Feb 2018

Senior Data Scientist


Did research and development in the following fields regarding the product improvement:

  • Language detection based on keywords from user searches. Researched state-of-art approaches, created different architectures of possible solutions, implemented approach which significantly improved existing language detection system. Technologies: NLP, Python, Spark, n-grams, Word2Vec, TensorFlow, deep learning.
  • Reinforcement learning agent for bid automation. Researched modern RL architectures, processed deep RL articles, proposed a solution based on state-of-art approach, built a couple proof-of-concepts (prototypes on demo environment). Developed a production solution, integrated it to data pipeline (built on Spark and HDFS + S3) and added improvements to it.
    Technologies: Python, TensorFlow, Keras, deep reinforcement learning, deep learning, gym (openai).
Apr 2016Mar 2017

Data Scientist


Did research and development tasks in several areas on the following projects:

  • Researched into state-of-the-art approaches to sentiment analysis in different domains, semantic orientation, dependency parsing, feature preprocessing (tfidf, bag-of-words), machine learning algorithms and evaluation methodologies.
    Used Python, BeautifulSoup, Pandas, Sklearn, NLTK, Pattern, WordNet, SentiWordNet, SpaCy and various other libraries during the research and development of SA model. Created algorithms on Python implementing research articles in the area and compared accuracy and other evaluation metrics of resulting models. Developed customary metrics corresponding to business objectives. (5 months: October 2016 – February 2017)
  • Investigated modern approaches to design of recommender systems (User-User, Item-Item, Content-Based Recommenders, SVD as well as Critique-Based Recommenders). Developed a hybrid recommender system on Python (Pandas, sklearn, Neo4J, flask). (5 months: May 2016 - September 2016)
  • Developed a prototype system for facial recognition (OpenFace library).
  • Developed web service prototype for flight delay prediction in the USA using machine learning (Delay prediction for USA flights project). (2 month, April 2016 – June 2016)
    Responsibilities: data wrangling and mingling, algorithms (SVM)
    Technologies: R, machine learning, Data Table, dplyr
Aug 2015May 2016

Software Engineer


Two similar BPM projects for process automation in banking (credit and scoring automation)

Responsibilities: bpm logic development, database connectivity, development of adaptive UI.

Technologies: Java, Pega PRPC, BPM, DB2, HTML, CSS, Javascript

Nov 2014Aug 2015

Junior Data Scientist


Several projects utilizing data analysis, data mining, aggregation, NLP (parsing):

  • Instant language classification of speaker for multilingual conference/meeting (Spoken language recognition project). (10 months, November 2014 - August 2015).

    Responsibilities: core programming, algorithm development, feature engineering, data mining, research
    Technologies: Flask, JS, Angular, sklearn, XGBoost, audioBasicIO

  • Development of chatbot for language learning purposes for non-native speakers (Language chatbot project). (8 months, January 2015 - August 2015).

    Responsibilities: integrating front-end and backend, NLP, research
    Technologies: Django, JS, Angular, sklearn, nltk, Mongo, wordnet

Sep 2014Dec 2014

Valuation and Business Modeling Intern


Valuation models improvement, business models development, DCF valuation, market approach, transactions approach, economic impairment tests, macroeconomic and industry analysis. (Several large valuation projects including Naftogaz assets valuation)
Excel, VBA, PowerPoint, Word.

Dec 2013Sep 2014

Software Developer


Internet store development, Netty Server (gaming server) development, ExUaDownloader (mini app) development and other projects.
Java, Android, Python


Nov 2016Current

PhD in Computer Science (expected)

Glushkov Institute of Cybernetics (GIC) of National Academy of Sciences of Ukraine (NASU)

Research of optimal control via apprenticeship learning and deep reinforcement learning approaches to optimal control.

Applications of deep reinforcement learning to robotics

Sep 2014Jun 2016

MSc in Applied Mathematics and Physics

Moscow Institute of Physics and Technology (State University)

Development of software and algorithms for analysis of frequencies of variation alleles in Human genome (genomic data science project): Python (BioPython), Java, R (knitr, bioconductor, plyr, dplyr, tidyr)

Sep 2010Jun 2014

BSc in Applied Physics

National Technical University of Ukraine 'Kyiv Polytechnic Institute'

C++, HTML+CSS, JavaScript, UML, XML, Java, Matlab (OCR), Numerical Methods, Statistical Data Analysis

Sep 2006Jun 2008

A levels

Kent College, Canterbury

A-level Maths, Physics, Chemistry, Further Maths, AS – level Economics

Additional education

Reinforcement Learning in Finance by New York University Tandon School of Engineering (expected 2019)

Practical Reinforcement Learning (with honors) by National Research University Higher School of Economics (2018)

Control of Mobile Robots by Georgia Tech (2018)

Deep Learning Specialization by (2017):

 - Neural Networks and Machine Learning 
 - Structuring Machine Learning Projects 
 - Improving Deep Neural Networks: Hyperparameters tuning, Regularization and Optimization
 - Convolutional Neural Networks
 - Sequence Models

Data Manipulation at a Scale: Systems and Algorithms course by University of Washington (2016)

Recommender Systems course by University of Minnesota (2016)
Introduction to Big Data course by University of California, San Diego (2016)
Algorithmic Toolbox course by University of California, San Diego (2016)

Data Science Specialization by John Hopkins University (2016):
 - The Data Scientist’s Toolbox
 - R Programming
 - Statistical Inference
 - Data Mining

Python For Everybody Specialization by University of Michigan (2016):
 - Getting Started with Python
 - Python Data Structures
 - Using Python to Access Web Data
 - Using Databases with Python

Machine Learning course by Stanford University (2015)
Java Advanced Course - ArtCode study center (April 2015 – June 2015)