Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 2

Courses:
Coursera – Python Basic Data Processing and Visualization byUniversity of California San Diego
Coursera – Tensorflow for AI and Machine Learning bydeeplearning.ai
Coursera – Convolutional Neural Networks in TensorFlow bydeeplearning.ai
Udemy – Python for Data Science and Machine Learning Bootcamp

Achievements:
– Sikorsky Challenge – startup "Greenales pot" (2020)
– Yangel Acceleration – space startup "Greenales" (2020)
I am skilled in machine learning, deep learning, statistics and analytics. Experienced in NLP, and CV tasks. I have certain Technical skills:
- Python, HTML, CSS;
- Data Processing Libraries: NumPy, Pandas, collections, itertools and other distributed processing tools;
- Creation, proof and refutation of hypotheses
- ML/DL libraries: TensorFlow, PyTorch, Transformers, SpaCy, XGBoost, Hugging Face, and other related packages;
- Data visualization libraries: Seaborn, Matplotlib, Plotly;
- Statistical data analysis, creation, verification and proof of hypotheses;
- NLP: Parsing unstructured documents, data mining, entity extraction and sentiment analysis;
- Strong knowledge of semantic analysis algorithms and libraries such as NLTK, Gensim, different variations of BERT, packages and algorithms for embedding extraction, pre-processing techniques, etc;
- Worked with AWS Comprehend, AWS Textract;
- Some experience with SQL queries;
- Experienced in developing microservices using Docker and FastAPI.
- CV: experience with computer vision algorithms and libraries, including OpenCV and YOLO;
- Skilled in working collaboratively with cross-functional teams and effectively communicating complex subjects in simple terms.

Worked on such projects as:
- Created a parser for CVs using variaties of NLP techniques such as parsing, scraping, pre-processing, semantic analysis, syntax-based rules, chanking, NER, sentiment analysis, etc.
- Developed an NLP text tone and semantic analyzer capable of detecting tone and sentiment of text data using text preprocessing, word embeddings, and machine learning algorithms. Utilized NLTK, Gensim, SpaCy, TensorFlow, Keras, BERT, GloVe, Word2Vec, and FastText libraries to achieve high accuracy rates in detecting positive, negative, neutral, fear, disgust, happiness.
- Computer vision tasks, such as image classification, object detection, and segmentation (using algorithms on text documents);
- Utilized computer vision techniques to perform floor area detection in room images. Used Python libraries such as OpenCV, NumPy, and Matplotlib for image preprocessing and analysis. Applied edge detection algorithms and Otsu's thresholding to effectively segment the floor area and enhance accuracy