Position: Data Scientist, Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 3

1. Master of Science in Computer Science and Data Analytics * Dual-degree program * Computer science degree from George Washington University, Washington, DC, United States.* Data Analytics degree from ADA University, Baku, Azerbaijan. *  
2. Bachelor of Science degree in Computer Science from ADA University. * graduated with magna cum laude honors (3.82 out of 4.00)
3. Published my first paper during my bachelor studies. * Title of paper: "Training and Evaluation of Word Embedding Models for Azerbaijani Language" * Presented the paper in the 8th Machine Intelligence and Digital Interaction Conference. * Later on published in Springer Nature Switzerland journal 
4.   Developed and maintained various Computer Vision Systems for production including but not limited to Face Recognition System, ANPR System, and OCR software which are currently being used nationwide. I wouldn't distinguish any of my achievements as one above another, but I would say my biggest accomplishment is going fast and steady in my field and hopefully learn and gain even more experience in the next 5-10 years.
* Responsible for the Maintenance of AI Systems that are developed and used by the company.
* Main Contributor for ML projects for the company by employing contemporary technologies in Deep Learning:
 1. Automatic Number Plate Recognition System:
      - Gathered around 30000 images from CCTV cameras and Open Source Datasets
      - Trained Models for Number Plate Detection Software, YOLOv4, and YOLOv4Tiny
      - Used Easy-OCR and MobileNet Classification Model for number plate recognition
  2. Face Recognition Software:
      - Developed RetinaFace model with the MobileNet backbone for face detection and localization.
      - Trained and Used ArcFace model (Resnet-50 backbone) for face encodings
  3. Built an OCR software for Electronic Governance System.
      - Retrained Tesseract-OCR to perform better on custom fonts that are used in the system.
      - Software is Mainly used to understand the context of scanned documents
      - Employed NLP and Data Mining techniques to retrieve text summaries from such documents.
   4. Composed Static Word Embeddings for various uses such as Semantic Analysis, Spam Detection, Text Classification etc.
      - Word Embeddings trained on Azerbaijani language.
      - Gathered data from Various newspapers, books, and novels with overall 164 million word tokens
      - Corresponding paper has been shared in my ResearchGate profile.
My expectation is to be a part of an innovative team where we can work on ambitious projects by experimenting and learning new technologies while being able to effectively exploit our expertise.