Olena

OLENA HRYHORIEVA
Junior Data Analyst

Place of residence: Lviv, Ukraine
Tel: [open contact info](look above in the "contact info" section)
Viber/WhatsApp/Telegram
E-mail: [open contact info](look above in the "contact info" section)
Linkedin: https:// [open contact info](look above in the "contact info" section)

SUMMARY

Junior Data Analyst with hands-on experience in data science, statistical
research analytics, product analytics. Used Python (Pandas, NumPy), SQL,
Google Sheets for data cleaning, analysis, and visualization. Looking for a job
where I can apply my analytical skills and support data-driven business
decisions.

HARD SKILLS

Conversion Rate, ARPU (Average Revenue Per User), LTV (Customer Lifetime
Value), Retention Rate, Churn Rate, Data Visualization, Data Storytelling, Data
Cleaning, Data Validation, A/B Testing, SQL, Python, Statistics.

TOOLS

Pandas, NumPy, Matplotlib, Seaborn, PostgreSQL,BigQuery, DBeaver,
Tableau, Power BI, Looker Studio, Jupyter Notebook, Google Sheets, Excel.

LANGUAGES

English - pre-intermediate
Ukrainian - Native
Russian - Native

PROJECT EXPERIENCE

User retention rate estimation using Google Sheets and SQL based on
Cohort Analysis.
https://docs.google.com/spreadsheets/d/1AvghF3Q49UoGadYFIZXPrhIPnG
bDAq4oVBIzYzF3V0A/edit?usp=sharing
Tools: SQL, Google Sheets.
Merging tables and calculating user cohorts. Building cohort tables in Google
Sheets using pivot tables. Calculating user retention rate. Comparing
behavior of different user groups (promotional vs. organic). Creating
interactive dashboards using Slicers. Drawing conclusions based on cohort
analysis.
Result: Provided data-driven analytics to support business decisions.

Historical data analysis tasks.
https://drive.google.com/file/d.1O8TA2yiSb46XvO5-qLxCBoK-AnkaeOUn/vi
ew?usp=drive_link
Tools: Numpy, Pandas, Matplotlib and Seaborn.
I conducted an initial data analysis: checked for missing values, reviewed data
types, explored feature distributions, and analyzed their impact on passenger
survival (Survived). Then, I performed data cleaning and feature engineering
(working with Age, Cabin, and FamilySize), after which I built a baseline
classification model to predict survival and evaluated its
performance.
Result: The analysis and modeling allowed me to identify key factors
affecting survival and achieve a baseline prediction accuracy for passenger
survival on the Titanic dataset.
WORK EXPERIENCE

GROCERY STORES Sales manager May.2012 - Aug.2021
●​ Monitored sales plan performance and analyzed daily revenue
indicators.
●​ Collaborated with suppliers and placed product orders according to
demand.
Result: Increased sales volume by 25% by optimizing the product assortment,
implementing a more efficient inventory planning system, and improving
customer service standards. Ensured proper product merchandising and
maintained high customer service standards.

INS-VIDEO Accountant Jan.2003 - Sep.2006
●​ Processed payroll, sick leave, and vacation payments for employees.
●​ Monitored the accuracy of primary documentation and cash reporting.
●​ Ensured timely and accurate payroll calculations, minimizing errors and
improving the quality of financial reporting.
Result: Optimized cash accounting and payroll processes, reducing
calculation errors by 30% and accelerating monthly reporting preparation.
EDUCATION

Junior Data Analyst Junior Test Engineer
"IT School GoIT" “IT Soft Development”
nov. 2025 apr. 2004