Applied scientist with 6+ years’ experience of applying machine learning techniques (Deep Neural Networks, Decision Trees and Ensembles, GenAI etc.) solving time series forecasting, optimization, anomaly detection problems. Driving project that revolutionizes forecasting models that relied on by the organization of 500+ people by replacing existing linear regression models with deep learning models (DeepAR+, CNN-QR, etc.). In-depth knowledge of big data analytics with 10+ years’ experience of big data processing on variety of data platforms. Two times winner of MLU (Machine Learning University) competitions, 1st place in a classification problem applying ensemble algorithms (30+ people competed), 3rd place in Computer Vision problem applying CNN algorithms (70+ people competed).
As a Senior Applied Scientist at AWS Networking's Network Capacity organization, I lead a team focused on advanced forecasting solutions for data center planning. In past two years our team rebuild the entire forecasting services by replacing the rule-based approach with state-of-art ML algorithms including time series forecasting algorithm, quantile regression approach for cold-start scenarios as well as variety of anomaly detection techniques. Since Nov 2024 I started a project that solves network rack/device placement optimization problem. A new project coming to me in 2025 to predict network link failure using Graph Neural Network.
Drove innovation on big data analytics platform, providing insights for critical decision-making throughout the company by processing and analyzing tera-bytes data each day.
Lead and focus on web analytics on big data platform, ie, Google Cloud, Hadoop/Hive with extended technology such as Tableau, MS SQL server, MySQL etc