Position: Computer Vision Engineer / ML Engineer
Category: Data Science
English level: upper
Years of experience: 2.5

- completed several courses for Computer Vision, Machine Learning, and SQL competence
- English (IELTS - overall 7.5 С1)
- created stable solutions for CV tasks that showed stable performance in different environments
- suggested a process optimization inside team workflow that was successfully implemented and used on a daily basis
- conducted risk assessments of credit portfolios with over 100 000 entities
- automated a significant portion of the evaluation process
I am an experienced Computer Vision Engineer with a track record of creating safety-checking modules using object detection, pose estimation, segmentation, and classification approaches. I am proficient in both classical CV techniques and Deep Learning for object tracking and segmentation and have worked extensively with remote servers to set up environments for training and inferencing deep learning models. I have a strong interest in research and continuously strive to improve the performance of my modules, implementing the latest approaches into production-ready products. Additionally, I have experience in leading a small team of developers, integrating processes for testing models, and ensuring proper data storage. My key toolset includes PyTorch, YOLO, OpenCV, MMPose, norfair, numpy, git, and dvc.

Previously, I worked as a Junior Data Analyst, where I wrote advanced SQL queries to extract data from data warehouses, conducted risk assessments of portfolios, evaluated potential returns with statistical analysis, and automated the data preparation process and exploration analysis for new portfolios using Python (pandas, numpy, matplotlib). I have experience building operational analytical reports using Excel and visualizing results in PowerPoint. My key toolset for this role includes SQL, pandas, numpy, matplotlib, and other data manipulation libraries.
I am preferably looking for a challenging project in the Computer Vision area (interested in discovering and applying not only Deep Learning approaches but also classical ones). 

Also, I would gladly discuss opportunities for Machine Learning projects in varying domains.