Position: Data Analyst, Product Analyst
Category: Data Analyst
English level: upper
Years of experience: 2.5

Academic projects:
* A complete cycle of real-time data collecting, preparing, fitting, and forecasting project about NBA season statistics and forecasting matchup outcomes, using Docker, BeautifulSoup4, FastAPI, and Scikit.

* Complete cycle but with finetuning LLM instead of building from scratch for Named Entity Recognition problem for matching CV and Job descriptions from 5+ web resources.
Ukraine agriculture time series data decomposition and research of the best performance model.

* One semester in New York Tandon School of Engineering, studying data science and analytics.

Personal achievements:
* 6 months as an Analyst in a European product startup in the domain of Health&Beauty. Сovered all the analytical and data engineering questions by myself and collaborated with developers to improve the back end regarding data manipulation and communication with investors and B2B clients.

* 1+ year of experience in an outsourcing company, direct communication with stakeholders, transforming business requirements into technical tasks in Jira.
Data / Product analyst with 2+ years of solid business experience in product/outsourcing companies, freelance
+ 1 year of related academic and internship experience.

I can work either in a team or on my own, establishing ETL pipelines (preferably GCP) and building real-time refreshing dashboards in Tableau/Looker/Looker Studio/Power BI with clear delivery to the end-user.

I used to work with freemium-modeled mobile applications, performing product analysis (LTV, churn rate, users stickiness, conversion rate, etc.) and events setup (Amplitude/Google Analytics).

I also worked as a marketing analyst, creating data extraction scripts with Python and analyzing marketing campaigns launched in META Pixel or TikTok.

Last but not least, I perform solid and precise ad-hoc analysis, including specific approaches from the perspective of Probability & Statistics, Linear algebra, classic data science, algorithms, and economics.