Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 3


Recently Completed Projects:
1. Armenian-based bank - The project was about a loan default prediction in the bank. This project's goal was to build a machine learning model that can predict if a person will default on the loan based on it and the personal information provided. The model was intended to be used as a tool for making decisions on issuing loans so that the risk can be lowered and the profit can be maximized. There were different functionalities from data gathering from different sources to training various models. Finally, a Gradient Boosting model was built to predict loans' probability of default. The working process involved a lot of communication with business owners, retail banking colleagues, planning, and development.
Languages: Python, SQL
Environments: PyCharm IDE, Libs: NumPy, Pandas, Scikit-Learn, Jupyter Notebook
Position: ML Engineer

2. US-based information technology company - In this machine learning project, open-source datasets that are publicly available were taken, and various methods/techniques of performing time series forecasting were discussed. The project aimed to research and develop several ML models to help the traditional methods such as the holt-winters method, autoregressive integrated moving average method, exponential smoothing methods which were discussed and avoided for Time Series Prediction with data preprocessing tools.
Languages: Python
Environments: PyCharm IDE, Libs: NumPy, Pandas, Scikit-Learn, Jupyter Notebook
Position: AI/ML Engineer

3. Germany-based biopharmaceutical industry - The project was about building a solution which will search necessary ontologies in a database of public articles and generate results as an outcome of findings. There were several assumptions agreed upon regarding the project.
Languages: Python, SQL
Environments: PyCharm IDE, Libs: NumPy, Pandas, Scikit-Learn, FuzzyWuzzy, Spacy, Dask Jupyter Notebook
Position: ML Engineer

4. International financial institution - In this Data Analytics project, the company sought to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. We found descriptive analytics to be insufficient for informed, data-driven decision making. As such, diagnostic and predictive analytics are increasingly important to organizations.
Languages: R, Python, SQL
Environments: PyCharm IDE, Jupyter Notebook, Python Libs: NumPy, Pandas, Scikit-Learn, R libs: dplyr, tibble, ggplot2
Position: Data Analyst