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

Developed GPT-3 based product about which an article was written. Developed NER model that predicts specific parts in text data with 98% rate.
Currently develop game where every NPC is a GPT-3 based model and auto-trading bot. Developed recommendation system for sales department, NN that predicts sales volume by seasonality, algorithm that finds best selling pairs of goods for one of the biggest retail chains in Ukraine. Developed NN that predicts type of product by description in custom database, and neural network that finds facial features for one startup. Developed DL models for NLP tasks, multilabel text classification and text generation. Rebuilt improved automatisation data streaming pipeline. Developed recommendation system for sales department of one of the biggest retail chains in Ukraine and neural network that predicts the product type by its description. Also developed model that extracts face features and classifies goods from customs database, that imported in and exported from Ukraine.

Have an experience in:
- Developing and training NER and text multilabel classification models
- Training text generation model
- Developing recommendation system for sales department of one of the biggest retail chains in Ukraine
- Making datasets for model training, evaluation and testing
- Developing and training face keypoint detection model
- Developing and optimisation ETL pipelines
- Data analysis: processing, visualisation and evaluating
- Computer vision: classification, semantic segmentation, object detection
- Deep Q-Learning
- Optimisation of neural networks
- Exploratory data analysis
- ML algorithms: regression, classification and clustering

Tools and Technologies:
Deep Learning: Fully Connected Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks
Deep Learning architectures: 
- Computer Vision: VGG, U-Net, ASPP,  R-CNN, SSD
- Neural Language Processing: LSTM, Attention, Transformer
Machine Learning: Logistic Regression, SVM, Naive Bayes, Random Forest, Decision Tree, XGBoost, kNN, K-means, DBSCAN, PCA, SVD, t-SNE
Languages: Python, SQL
Tools: NumPy, Pandas, scikit-learn, TensorFlow, Keras, Tensor-RT, nltk, spaCy, BeautifulSoup, Parallel processing, OpenCV, Kivy (a little bit).
Visualization tools: matplotlib, seaborn
I would like to develop DL models for any applications. I do not want to spend a lot of time labelling and analysing data.