Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 4

• CleaningTool: Developed an image processing application that detects duplicate photos and identifies blurry images. This project utilized deep learning and computer vision techniques to analyze large collections of photos and provide accurate results. (02/2023-Present)
• Wow-Shop: An E-commerce Business Project during work developed and implemented a technical system to streamline business operations resulting in over 140,000 shipments made to customers over a period of more than 2 years (04/2020 - 12/2022).
• TradingTool: A cryptocurrency trading bot programmed in Python that utilizes various indicators and strategies to automate the buying and selling of digital assets in real-time.(05/2018-11/2018)
• FootballVideoAnalysis: A program was developed and implemented for football analysts of the FC "Dnipro" team, which helped to analyze the game for technical and tactical actions of football players. (09/2014 - 05/2015)
Timeseries forecasting using Python to forecast trends, identify patterns, and extract insights from complex datasets
• Conducted topic modeling tasks using tools such as Latent Dirichlet Allocation (LDA) and BERTopic to identify themes and patterns in large datasets of unstructured text data
• Contributed to data-driven decision-making and provided valuable insights through the application of data science techniques and tools.
Worked on semantic search for the site (research new methods and implement it). Fine-tuned language models (BERT, GPT-2)
• Collaborate with product and development teams to understand company needs and devise possible solutions for buyers and sellers
• Design, experiment and implement models and to maximize customer experience, business outcomes, and infrastructure readiness.
Create and implement voice authentication system
• Assisting and leading the implementation of the face authentication system
• Architect and develop end-to-end solutions including data preparation, machine learning pipelines.
Developed and implemented predictive models across various banking areas, including behavioral scoring, collection scoring, application scoring, and client voice verification models. Using the developed behavioral scoring model, over 6 months, more than $1.5 million was saved compared to the previous model used. Processed, cleaned, and checked data integrity for analysis purposes
•Standardized and automated model-building processes, covering all stages from data collection and preparation to deployment to production
• Conducted economic efficiency assessments of the implemented models
• Developed and implemented a voice-based customer authentication model for phone calls made to the call center.
Conducted analysis of product solutions to evaluate their effectiveness and overall efficiency.
• Performed forecasting and evaluation of experimental results to drive data-driven decision-making.
• Analyzed product processes and metrics to identify areas for improvement and make recommendations to optimize performance.
• Generated reports of varying levels of complexity to communicate insights and results to relevant stakeholders.