Position: Data Scientist / ML Engineer / Computer Vision / NLP
Category: Data Science
English level: fluent
Years of experience: 2

Obtained a bachelor's degree in applied mathematics at the Taras Shevchenko National University of Kyiv, received a diploma with honors.

Studying at University of Tartu is founded by a Ministry of Foreign Affairs of Estonia. In addition received an achievement stipend for remarkable study performance.
Research fellow in bioinformatics at University of Tartu (Tartu, Estonia; Dec. 2021 - PRESENT)
• Applying computer vision methods for biomedical data analysis, mainly for tasks of cell object detection in microscopy images. 
• Applying state-of-the-art object detection models (e.g. YOLOv5) that leads to beating published performance benchmarks.
• Studying the importance of annotation completeness and its influence on model performance.
• Performing label-efficient object detection in limited data and annotation settings.

Research & Development Engineer at CIENCE Technologies (Kyiv, Ukraine; Feb. 2021 - Aug. 2021)
• Main duties: automation, data mining, warehouse management, reporting.
• Developed solutions in Google Cloud Platform (mainly worked with BigQuery, Cloud Functions, AI Platform).
• Worked with fuzzy matching algorithms over leads' data: record linkage & record deduplication.
• Participated in entity resolution NLP task: standardization of main leads' features (job title, industry, geographical location).
• Implemented advanced SQL queries for lead search automation and speed optimisation.
• Delivered reports over CRM data to executives.
• Calculated SaaS metrics and scoring models.

Data Scientist at BI Smart (Kyiv, Ukraine; May. 2020 - Jan. 2021)
• Built recommendation systems based on sequential and association rules for a pharmacy network. Just an appropriate SMS notification at suitable time with a suggestion to buy specific goods that customer might be interested in yielded significant extra revenue.
• Built geomarketing models for detecting potential profitable placements of pharmacies and medical clinics. Models included surrounding infrastructure, rivalry situation and user-specified characteristics. Processing pipeline also involved performing geocoding operations. This application provided business with an extra boost in expansion-related decision-making.
• Applied RFM algorithm for customer segmentation. Helped a company to apply different communication patterns for treating clients.
• Improved a call center efficiency via detecting potential buyers. It helped to achieve the highest revenue a company has ever had.
• Predicting customers prone to churn. Business applied special treatment for fetched customers.
• Built a mailing advisor system for detecting clients  susceptible to offers. Advanced an automated clients selection for a daily mailing.
• Implemented a system for VIP customers early detection.
NOT Data Engineering