Rudolf Eremyan

Created interactive customer insights dashboards by developing data collection tools, conducting sentiment analysis on the gathered datasets, and constructing an engaging and user-friendly dashboard using AWS QuickSight.

Designed and implemented automated data pipelines on AWS for gathering information from diverse social media platforms as part of an internal social media monitoring service. Developed insights extraction analytics metrics and presented them through an interactive dashboard tailored for the product team.

As a data engineer and a quantitative researcher/developer at a hedge fund, I created a data model by translating financial Excel sheets and business requirements, implemented a multi-user interface on a widely used cloud service to manage assets and data within the database efficiently, and I also established data pipelines to collect financial data from diverse banks and financial services. Additionally, I developed reporting mechanisms and internal communication services to enhance data accessibility and communication within the organization.

I employed natural language processing algorithms and time series analysis techniques to create a model for early trend detection within the Twitter stream. I also crafted scripts utilizing the Twitter API to extract and analyze data from the Twitter stream. Then, I enhanced the interpretability of the results by visualizing the analysis outcomes through various plots.

I implemented attribution modeling, a technique to assess the financial influence of communication on key business objectives such as sales, customer retention, revenue, and profit. I also utilized SQL extensively for data manipulation and analysis, along with Python and R libraries.

I developed scripts for data migration and client notifications and implemented data integrity tests to ensure the completeness and accuracy of existing data. During this project, there was effective collaboration between me and an international team spread across different geographical locations.

Using Google BigQuery analyzed news big dataset.

Trained machine learning models for text classification which used in text filtering mechanism. Integrated cloud ML services such as IBM BlueMix and Google Cloud NLP with an existing application.

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

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

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.

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"

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.

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

Worked with clients team building new database for the pharmaceutical industry, by collecting, cleaning and managing data from different sources. Used AWS services for implementing ETL, storing logs, etc.

Parsed and analyzed a large volume of athletes' performance data. Applied the Monte Carlo statistical approach on athletes' performance data for simulating track and field competitions. Used AWS cloud services for running computations and storing generated results.