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Rudolf Eremyan, Data Science Developer in Tbilisi, Georgia
Rudolf Eremyan

Data Science Developer in Tbilisi, Georgia

Member since July 3, 2018
Rudolf is a data scientist with more than four years of experience in NLP, and 2+ years in machine learning. He has developed the first chatbot framework for the Georgian language and other AI-based tools which have been adopted by Georgia's largest banks and the government.
Rudolf is now available for hire



  • Python, 4 years
  • NLTK, 4 years
  • SQL, 4 years
  • Machine Learning, 4 years
  • Pandas, 3 years
  • Scikit-learn, 3 years
  • Statistics, 3 years
  • XGBoost, 2 years
Tbilisi, Georgia



Preferred Environment

Linux, Git, pyCharm

The most amazing...

...framework I've developed is a chatbot framework for the Georgian language.


  • Data Scientist

    2016 - 2018
    Pulsar AI
    • Developed a chatbot framework for Georgian language.
    • Created an automated news article grouping tool.
    • Designed a tool for sentiment classification on texts from social networks.
    • Worked with time series for analyzing and predicting cryptocurrency price.
    • Analyzed data and presented results in a clear manner.
    Technologies: Python, Scikit-learn, Gensim, NLTK, Keras, fastText, spaCy, Pandas, Numpy, Docker, Git, MongoDB
  • Software Developer Internship

    2016 - 2016
    Virtuace Inc.
    • Fixed bugs.
    • Expanded functionality of the existing application.
    • Tested new modules.
    Technologies: Linux, Git, Java, Tomcat, XML
  • Full Stack Software engineer

    2014 - 2016
    Georgian Technical University
    • Developed the front-end for managing and working with linguistic corpora.
    • Created web services for operating with linguistic corpus data.
    • Organized database structure for storing and manipulating the linguistic corpora.
    • Analyzed documents using NLP tools and presented results in a clear manner.
    Technologies: HTML, CSS, JavaScript, REST, MySQL, Corpus linguistics, NLTK, Python


  • Chatbot Framework for Georgian Language (Development)

    Ti-Bot, the first ever Chat Bot to speak Georgian.

  • Automated News Article Grouping Tool (Development)

    News article grouping tool uses word vectorizing technologies with a combination of clustering algorithms for automatically grouping similar articles parsed from news websites.

  • Social Media Sentiment Analysis Tool (Development)

    Social media sentiment analysis tool is a combination of natural language processing technologies and machine learning algorithms for predicting of the sentiment for comments and posts, collected from social networks such as Facebook and Instagram.

  • Spell Checker for Georgian Language (Development)

    Spell checker tool uses classical algorithms with a combination of powerful machine learning and natural language processing methods for detecting and correcting mistakes in the sentences. This product used by the largest companies in Georgia for detecting and correcting mistakes in documents.

  • Cryptocurrency Prices Monitoring Tool (Development)

    Cryptocurrency prices monitoring tool uses time series analysis algorithms and Tweeter API combined with NLP tools such as Sentiment analysis, for monitoring and predicting price movements of Bitcoin and other cryptocurrencies.

  • NLP Tool for Automatic Identification of Georgian Dialects (Other amazing things)

    A tool used for automatic identification of the Georgian dialects in documents from different sources such as forums, social networks, etc. It's based on machine learning classification methods and NLP approaches. During development, I worked with a group of linguists who prepared training and evaluated data for a classification model.

    This project was awarded the "Best Scientific Research of the Tbilisi State University 76th Student Conference"

  • Linguistic Corpus Management System (Development)

    Developed a web application for storing, manipulating, and analyzing linguistic data.

  • Four Pitfalls of Sentiment Analysis Accuracy (Publication)
    Manually gathering information about user-generated data is time-consuming, to say the least. That's why more organizations are turning to automatic sentiment analysis methods—but basic models don't always cut it. In this article, Toptal Freelance Data Scientist Rudolf Eremyan gives an overview of some sentiment analysis gotchas and what can be done to address them.


  • Languages

    Python, SQL, JavaScript, Java, HTML, CSS, R, Bash
  • Frameworks

    Machine Learning, Flask, Selenium
  • Libraries/APIs

    Pandas, Scikit-learn, NLTK, Beautiful Soup, REST APIs, XGBoost, SciPy, NumPy, SpaCy, Google AdWords, Keras, Matplotlib
  • Tools

    Jupyter, Gensim, Trello, pgAdmin, Bitbucket, Git, GitHub
  • Other

    Text Classification, Text Mining, Data Engineering, Apache Superset, Computational Linguistics, Regular Expressions, Clustering Algorithms, Topic Modeling, Web Services, Data Mining, Google BigQuery, SSH, Web Scraping, Statistics, Data Structures, Algorithms, IBM Cloud
  • Paradigms

    Scrum, REST
  • Platforms

    Linux, MacOS, AWS EC2, Docker
  • Storage

    PostgreSQL, MongoDB, MySQL, AWS S3


  • Bachelor's degree in Computer Science
    2013 - 2017
    Tbilisi State University of Ivane Javakhishvili - Tbilisi, Georgia
  • Google Analytics Individual Qualification
    DECEMBER 2018 - DECEMBER 2019
    Digital Academy for Ads
  • Deep Learning Summer School
    JULY 2017 - PRESENT
    University of Deusto
  • Deep Learning Nanodegree
  • Machine Learning Online Course
    Stanford University
  • Language and Modern Technologies
    Goethe University Frankfurt/Main
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