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

CERTIFICATES:

* Python - The Practical Guide
* Docker and Kubernetes: The Practical Guide
* How Google does Machine Learning
* SQL for Data Science
* Structuring Machine Learning Projects
* Sequences, Time Series, and Prediction
* Neural Networks and Deep Learning
Work experience:

1. Machine Learning Engineer (04/2023 – 06/2023):
- Built an accurate image classification model using PyTorch, FiftyOne, FastDub, ClearML, BentoML, FastAPI, and Gradio.
- Developed an OCR model for a specific task using PaddleOCR and YOLOv5.

2. AI Engineer (10/2021 – 03/2023):
- Created an object detection model for waste classification using PyTorch, TFlite, OpenCV, and YOLO.
- Researched neural network-based solutions for generating 3D objects and animations from images using NeRF.
- Built models for price prediction and image denoising using various libraries and frameworks.
- Developed a custom image classification model using TensorFlow, Keras, OpenCV, and Docker.

3. Junior Data Scientist (01/2021 – 10/2021):
- Developed object detection algorithms using computer vision and deep learning techniques.
- Implemented object detection and hand tracking using OpenCV and YOLOv5.
- Worked on NLP tasks such as text recognition and classification.

4. Junior System Administrator (09/2020 – 11/2020):
- Provided technical support, installed software/hardware, diagnosed and solved network problems.

Personal projects:
- Deep learning: Developed neural network models for bird sound classification (graduation work).
- Numerical methods: Implemented the Implicit Milne's Predictor Corrector method for the Lotka-Volterra model (course project).
- Optimization methods: Explored the Conditional gradient method with automatic step selection (course project).
- Deep learning: Developed recurrent neural network models for air pollution forecasting (graduation work).
- DataKyt: In-progress project for recording equipment using Python and SQL.