Position: Data Scientist, Machine Learning, Data Engineering
Category: Python
English level: fluent
Years of experience: 1

Got a chance to be an intern at Google this year, helping to solve a challenging but important problem with abuse fighting.
Awarded a partial​ scholarship during the first year at university based on previous achievements.
Top 3%​ among U​krainian students ​based on entrance tests results (199/200).
Work Experience:

Summer Trainee Engineering Program Intern at Google, Zürich, Switzerland (July – October 2022)
As part of the Privacy, Safety, and Security team, developed a visualization tool that tracks abusive accounts and provides visualizations of their actions and characteristics. This tool is used by analysts to simplify their day-to-day work, resulting in increased quality of the abuse detection system in the long run.
Conducted research of potential solutions for the problem, as well as technical aspects, which led to results being of high quality.
Technologies Used: Google development stack, Python, Pandas, GoogleSQL, HTML, Typescript & Vega.



Speaking about my projects:

Image Stitching (February – June 2022)
Used Python, OOP & Linear Algebra Principles to implement an algorithm that composites multiple overlapping images captured from different viewing positions into one natural-looking panorama. 
Implemented from scratch horizontal and vertical stitching for several images using the K-Nearest-Neighbors algorithm & David Lowe's ratio test for Feature Matching, Homography estimation with Ransac algorithm, and SIFT algorithm for keypoint detection.

Sportify, Project Manager (February – June 2022)
Managed a team of 7 students. Performing competitive analysis and market research, defining a unique selling point (USP), and creating a strategy and implementation plan. Developed high-quality benchmarking, technical documentation, and mockup using Figma, resulting in an easy-to-use marketplace for coaches and sports clubs' clients, which increased demand for sports activities by 23%, according to a survey of coaches and gyms.
 
Spam Classifier (October 2021)
Used Python, Pandas, NLP principles, OOP & Multinomial Naive Bayes algorithm to develop a spam filter with 96% accuracy. 

Musical Telegram (April – May 2021)
Used Python & correlation ML model to develop a Telegram bot with Spotify API integration that simplified finding new music based on preferences for 300+ students.

Friends on Map, February 2021
Used Python & HTML to create a web application for friends’ locations map generation of any Twitter account by incorporating Twitter API and Flask framework.

I am an active volunteer with broad experience organizing and assisting in major events. Speaking about some of them:
Designer & Website developer at TEDxUCU
Organizer of Apps Summer Camp
Tech-Supporter for Lviv Data Science School
I would like to work in an open team where you can develop and gain experience from each other.
Passionate about gaining more knowledge and experience in Data Science, Python engineering, Computer Vision, Artificial Intelligence, and NLP.