Position: Machine learning Engineer
Category: Python
English level: upper
Years of experience: 5

Winning gold medals in mathematics 2 times among 30 countries
Can speak in English, Russian, Turkish, Azerbaijani very good. Understand French and Germany
Machine learning engineer
• Own, design, and develop AI ML pipelines: be responsible for efficient model training, testing, and monitoring
• Apply scientific knowledge to analyze and collect data, identify problems, devise solutions and construct methodologies
• Develop machine learning and deep learning models for tabular data
• Create and automate data pipelines including data extraction, validation & anomaly detection, indexing, and other data-related tasks
• Use and fit different mathematical and econometric models, develop descriptive and predictive models that deliver better decisions
• Collecting data using scraping tools

AI engineer at E-government Development Center 
• I created the algorithm with the help of OpenCV and applied image processing on the national id cards 
• After successful processing,I created the model with Pytesseract for the specific characters and applied it to the images 
• As the final stage,I built the API which as an input takes the picture of the ID card and returns the information like name, surname, father name, birthplace, birthdate, and id as the JSON file within 2-3 seconds 

Data Science Teacher at Data Scinence academy
• I coached 6 bootcamps in the Data Science Academy as a Teacher and Assistant. 
• As a teacher,I taught the basics of python: types of variables, comprehensions, loops,functions. 
• Scraping with both Scrapy and BeautifulSoup libraries and OOP were also covered in my lessons. 
• I instructed in my lectures the introduction to Data Science with algorithms like Linear and Logistic Regression, Decision Trees, Naive Bayes, kNN, and k-means as well. 

Machine Learning Engineer at Innovation Agency
• In a short time, my job consisted of creation 2 programs as "Sentiment Analyses for Azerbaijan" and "Face Recognition with mask" 
• In the first project,I scraped the data from AppleStore, Google Play, and Facebook, and created nearly more than 80000 sentiments. After preprocessing all the data,I identified the best NLP algorithm among the FastAI, Tensorflow, and PyTorch for this specified case 
• In the next project, "Face Recognition with mask",I learned a lot about the OpenCV library and generated multi-layer perceptron for the face recognition of the person even with mask

Data Science Intern at IKT LAB
• As an intern,I gained lots of experience in the basics of Data Science like Naive Bayes, Regression. During the internship received experience in both Python and excel, also worked with large datasets.