I am a Machine Learning Engineer and Data Scientist with solid mathematical training and several years of hands-on experience in production projects.
I build end-to-end ML solutions – from raw data and research to deployable models and clear reports that non-technical stakeholders can use.

Time series forecasting for retail and logistics (TSMixer, Prophet, LSTM, classic statistical models).
In my previous role I improved supply forecast accuracy by about 15%, which reduced stockouts and overstock.

Computer Vision and 3D scenes: city-scale Gaussian Splatting, depth-map processing, image-based reconstruction and quality improvement of large scenes (up to ~200M Gaussians).

Classic ML for regression and classification (Gradient Boosting, Random Forest, k-NN and others) with scikit-learn and PyTorch.

Data engineering for ML: collecting data from databases and APIs, cleaning and validation, feature engineering, exploratory data analysis, dashboards in Power BI.