Arshak Mkhoyan, Data Scientist and Machine Learning Developer in Yerevan, Armenia
Arshak Mkhoyan

Data Scientist and Machine Learning Developer in Yerevan, Armenia

Member since August 25, 2021
Arshak has 3+ years of experience working as a data scientist and machine learning developer. He helped businesses become more profitable by increasing the click-through rate through recommendation systems and the retention rate of marketing campaigns using uplift modeling. Arshak is looking for projects that allow him to work with data to get valuable insights and develop machine learning algorithms to solve business tasks, showing his proficiency in Python, machine learning, and deep learning.
Arshak is now available for hire

Portfolio

  • USMALL
    Recommendation Systems, Python, AWS, FAISS, Deep Learning, Data Analysis...
  • Globus
    Data Science, Machine Learning, SQL, Microsoft Excel, Python, Docker...
  • BetConstruct
    Python, Data Science, Machine Learning, SQL, Deep Learning...

Experience

Location

Yerevan, Armenia

Availability

Part-time

Preferred Environment

Jira, Slack, Jupyter Notebook, PyCharm, Linux, MacOS

The most amazing...

...project I've developed is a model for predicting customers' uplift (effect of communication) that increased the profitability of the marketing campaign by 20%.

Employment

  • Machine Learning Developer

    2021 - 2021
    USMALL
    • Developed an item-to-item recommendation system leveraging deep learning methods.
    • Researched eCommerce recommendation systems, state-of-the-art algorithms.
    • Analyzed user traversal patterns, item popularity, and user-item interactions.
    • Used AWS Sagemaker for model testing and training.
    Technologies: Recommendation Systems, Python, AWS, FAISS, Deep Learning, Data Analysis, Visualization, Pandas, NumPy, Scikit-learn, Gensim, Data Visualization, Machine Learning, Data Science, Jupyter Notebook, PyCharm, Linux, Natural Language Processing (NLP), Dashboards, Plotly, Docker Hub, Statistical Analysis, Jupyter, Data Analytics, Amazon Web Services (AWS), Data Pipelines, Data Engineering
  • Senior Data Scientist

    2020 - 2021
    Globus
    • Developed a model for estimating customers' uplift in marketing campaigns, increasing their profitability by 20%.
    • Created a churn prediction model which assists company operators in predicting customers who are most likely subject to churn.
    • Built a tool for finding similar groups of users to the one requested based on specific features.
    • Analyzed customer behavior, goods characteristics, and hypermarkets.
    Technologies: Data Science, Machine Learning, SQL, Microsoft Excel, Python, Docker, Data Analysis, Visualization, Statistics, Pandas, NumPy, Scikit-learn, Data Visualization, Jupyter Notebook, PyCharm, Dashboards, Plotly, Flask, Docker Hub, Statistical Analysis, Jupyter, Data Analytics, Data Scraping, Data Pipelines, Data Engineering
  • Machine Learning Developer

    2019 - 2021
    BetConstruct
    • Created an extractive question answering system based on NLP and statistical methods.
    • Built a bot detection system to identify parsers among regular users of the website.
    • Developed a chatbot system integrating several APIs and NLP-based methods.
    • Managed a team of three data scientists using Jira as a reporting tool.
    • Created a poker AI after researching reinforcement learning and game theory.
    Technologies: Python, Data Science, Machine Learning, SQL, Deep Learning, Natural Language Processing (NLP), AWS, Docker, Git, FAISS, Visualization, Elasticsearch, Google Cloud Platform (GCP), MongoDB, Selenium, PyTorch, Pandas, NumPy, Scikit-learn, Gensim, Data Visualization, Data Analysis, TensorFlow, Jira, Slack, Jupyter Notebook, PyCharm, Linux, Dashboards, Plotly, Flask, Statistical Analysis, Jupyter, Data Analytics, Statistical Modeling, Data Scraping, Data Pipelines, Data Engineering
  • Business Analyst

    2018 - 2018
    Ameriabank CJSC
    • Conducted weight adjustment on multiple survey data, ensuring accurately representative samples.
    • Analyzed available data by identifying main trends, changes, and root causes.
    • Visualized data and prepared reports for stakeholders.
    Technologies: Python, Excel 365, Tableau, HTML Parsing, Pandas, Matplotlib, Dashboards, Jupyter, Data Analytics, Business Intelligence (BI), Statistical Modeling, Data Scraping

Experience

  • Uplift Model for Marketing Campaigns

    Globus needed accurate information about the effect of their marketing campaigns' communication on customers' behavior.

    I developed a machine learning model for predicting customers' uplift in marketing campaigns. To built the model, I collected the data via A/B testing, extracted the insights, chose ML and business metrics to track performance, prepared data for modeling, created the uplift model, and tested this model in offline and online environments.

    Overall, this model helped to increase the profitability of marketing campaigns by 20%.

Skills

  • Languages

    Python, SQL
  • Frameworks

    Selenium, Flask, Hadoop
  • Libraries/APIs

    Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib
  • Tools

    Jira, Slack, PyCharm, Microsoft Excel, Git, Gensim, Jupyter, Plotly, Docker Hub, Tableau, BigQuery, Cloud Dataflow
  • Paradigms

    Data Science, Business Intelligence (BI)
  • Platforms

    Jupyter Notebook, Docker, Linux, MacOS, Google Cloud Platform (GCP), Amazon Web Services (AWS)
  • Other

    Data Analysis, Deep Learning, Machine Learning, Data Visualization, Statistical Analysis, Data Analytics, Statistical Modeling, Data Scraping, Economics, Natural Language Processing (NLP), AWS, Recommendation Systems, FAISS, Statistics, Data Engineering, A/B Testing, Excel 365, HTML Parsing, Dashboards, Big Data
  • Storage

    MongoDB, Data Pipelines, Elasticsearch

Education

  • Bachelor's Degree in Economics
    2014 - 2019
    American University of Armenia - Yerevan, Armenia

Certifications

  • Google Cloud Big Data and Machine Learning Fundamentals
    OCTOBER 2021 - PRESENT
    Google Cloud | via Coursera
  • Introduction to Deep Learning
    JULY 2019 - PRESENT
    HSE University | via Coursera
  • Machine Learning
    MARCH 2019 - PRESENT
    Stanford University | via Coursera

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