Position: Computer Vision / Deep Learning / Machine Learning Engineer
Category: Data Science
English level: upper
Years of experience: 7

Updated company’s mmdetection [GitHub] fork from 1.x to 2.x version
- Introduced unified training docker environment (current users approximately 10 people from different teams)
- Improved internal object detection project robustness with unit tests
- Improved internal project object detection model metrics (up to 10% AP for some classes)
- Made models conversion from FP32 to INT8 without decrease of performance and with 5x speed-up using OpenVino
- Created solution using VAE and CatBoost for anomaly detection on thermal power plant
that increased possible crash prediction on the plant (made full research cycle from communication with business to production solution)
- Made full cycle of research for Customer verification using computer vision (classification, object
detection with Pytorch), prepared POC versions of models in docker environment
- Created a semi-automated system for data labeling speed up (clouds detection + pre-segmentation)
- Created several dockerized versions of models a) for vehicles detection outside of the cities
b) for city buildings type segmentation
- Created robust deep learning models using UNet for agricultural fields borders prediction
- Leaded process company participation in the challenge Facebook Geo Workshop for CVPR 2018 ( - Participated on behalf of Company on Kaggle Airbus ships detection challenge - that helped company’s marketing
- Created salary cap model for globalfantasysports platform for basketball, golf, and American
football
- Created ML models for fantasy points prediction, an algorithmic model for automated team selection, ML models for match outcome prediction and performance of each player in the upcoming game
- Prepared full documentation with Python code examples for backend team on how and what data to use from data providers
- Introduced and maintained Knowledge Base about ML and analytics side of the project in Attlassian Confluence
- Made full research of competitors (e.g. Fanduel and DraftKings)
10 years of experience in different domains (Computer Vision, Data Science, Machine Learning, Deep Learning, Analytics, Business Intelligence)
Advanced knowledge of deep learning for computer vision tasks, machine learning methods (clustering, regressions, decision trees), statistics.
Machine Learning competitions - Kaggle Master, Top places on other platforms (TopCoder, Codalab, CrowdAI)
My main objective is to solve business problems and needs using Machine Learning / Deep Learning/ Computer Vision technologies, continue to grow as a data science and machine learning expert, continuously improve my programming skills. Extraordinary interesting tasks – this is what I am looking for.