Кирил

IVANOV KIRILL
Product Analyst | Data Analyst
[відкрити контакти](див. вище в блоці «контактна інформація») • [відкрити контакти](див. вище в блоці «контактна інформація») • @ivkir00

Summary

Data Analyst with a strong foundation in SQL, Python (pandas, numpy), data visualization (Tableau, Power BI), and an
understanding of business metrics. Hands-on experience processing datasets in BigQuery, developing interactive
dashboards, and applying mathematical statistics. English at the B2 level.

Skills

SQL • Power BI • Tableau • Python • Pandas • NumPy • Excel • Google Sheets • Power Query • DAX • Product Metrics •
Statistics • A/B Testing

Practical experience

Links to projects from the resume
ROI Analysis of Marketing Campaigns 09/2026
Technology stack: SQL, BigQuery, Tableau.
Created a unified marketing data mart by consolidating disparate tables to aggregate expenses and revenue.
● Calculated key business metrics (Total Revenue, absolute payback, relative ROAS/ROI), accounting for missing values.
● Built an interactive Tableau dashboard to quickly identify unprofitable campaigns, which enabled budget allocation
optimization and reduced analysis time by two hours per week.

Analysis of A/B testing results and automation of cohort analysis 09/2026
Technology stack: Python (pandas, numpy, scipy, matplotlib, seaborn).
Conducting the full cycle of A/B test analysis and developing an automated tool for the regular monitoring of Retention
Rate.
● A/B testing and statistical analysis: Verified the correctness of the traffic-splitting system (SRM) using goodness-of-fit
tests. Decomposed the target metric (ARPU) into its drivers (CR, ARPPU) to pinpoint the causes of changes. Employed
the Chi-square test, Welch’s t-test, and bootstrapping to calculate p-values and confidence intervals. Formulated
business recommendations regarding the advisability of the release.
● Building a Retention Heatmap: Developed a universal Python function for cohort-based churn analysis. The script
processes two datasets, segments users by registration date, and calculates retention for a specified period.

Exploratory analysis of sales data 09/2026
Technology stack: Python (pandas, numpy, scipy, matplotlib, seaborn).
Cleaned and prepared a year's worth of raw data: handled missing values, standardized data formats, and identified and
removed anomalies.
● Conducted exploratory data analysis (EDA): calculated average order value (AOV), identified seasonal trends,
top-selling products, etc.
Education

Zaporizhzhya National University Zaporizhzhia, Ukraine
Bachelor's degree, Economic Cybernetics (3rd-year) 2024 - 2028