Резюме Engineer – 400 $

Yevhenii ********

PYTHON DATA SCIENCE AND MACHINE LEARNING

ENGINEER

Показати контакти | https://github.com/Zheka-GGS |

www.linkedin.com/in/yevhenii-********

| Tbilisi, Georgia

SUMMARY

Junior Python Developer with hands-on experience in Data Science and Machine Learning. Successfully completed the

“Python Data Science and Machine Learning” program at GoIT and independently developed several end-to-end

projects, including a Customer Churn Prediction system (Random Forest / XGBoost, Streamlit interface, Docker), a

CIFAR-10 image classifier with custom CNN and VGG16 transfer learning.

HARD SKILLS

:

Programming Languages

: Python (1+ year), C# (6 months)

Data Science & Machine Learning

: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Supervised Learning (Regression &

Classification), Model Evaluation, Feature Engineering, Data Preprocessing and Visualization

Databases

: SQL, Database Integration

Web Development

: HTML, CSS, JavaScript (basics)

Other Technologies

: Aiogram (Telegram Bot Framework), RESTful API Integration, Git basics

Tools

: Jupyter Notebook, Python virtual environments

Languages

:

English — Intermediate B

1

Ukrainian — Native

Russian — C2

Polish — C1

PROJECT

EXPERIENTS

Telegram Bot (Personal Project)

2026

Developed a fully functional Telegram bot using Python and the Aiogram framework, ensuring high performance and

scalability through asynchronous architecture.

Integrated a robust SQL-based database for efficient data storage, retrieval, and user state management, along with

seamless connections to external APIs for dynamic data processing.

Designed and implemented multiple interactive

features, including command handling, user-friendly workflows, and real-time responses, while also documenting

architectural decisions and outlining clear pathways for future scaling and feature expansion.

GitHub: https://github.com/Zheka-GGS/Telegram-bot

EDUCATION

:

IT School GoIT

Python Data Science and Machine Learning

2025

.12

–

2026

.05

Interregional Academy of Personnel Management

Cybersecurity

(!)

2025

.09

—

2030.06

Customer-Churn-Prediction

2026

Developed an end-to-end machine learning system to predict customer churn for an internet service provider. The

goal was to identify high-risk customers early and support data-driven retention strategies.

Built and compared multiple classification models (Logistic Regression, Random Forest, XGBoost) on a real-world

dataset of ~1,000 customers with 9 behavioral and service-related features.

Achieved ROC-******** and F*********.74 with the selected Random Forest model.

Created a production-ready Streamlit web application that allows users to input customer data and receive real-time

churn probability with risk level (Low / Medium / High).

GitHub: https://github.com/Zheka-GGS/Customer-Churn-Prediction