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EVGEN KOVALOV
Python Backend / Data Scientist
Kyiv, Ukraine | [открыть контакты](см. выше в блоке «контактная информация») | [открыть контакты](см. выше в блоке «контактная информация»)
GitHub: github.com/exEKS

SUMMARY
Python developer with a strong analytical background and experience working with data, APIs, and backend
services. Skilled in building REST APIs, working with relational databases, performing exploratory data analysis,
and developing data-driven applications.
Combines knowledge of backend engineering (FastAPI, REST APIs, Docker) with data analysis and machine
learning foundations (Pandas, NumPy, scikit-learn). Interested in building reliable data products, analytical services,
and scalable backend systems.

SKILLS
Backend: FastAPI, Docker, REST APIs, model serving
Database: PostgreSQL, SQL(joins, aggregations, filtering, analytical queries) Basic database design

Machine Learning & Deep Learning: TensorFlow, PyTorch, Keras, CNNs, RNN, LSTM, Transfer Learning, NLP
Data & Evaluation: Data pipelines, Precision, Recall, F1-score, Accuracy, MAE, RMSE
Tools: Git, pytest, Streamlit, Postman

PROJECTS
Customer Churn Prediction System
Developed a machine learning system for predicting customer churn in a fitness club using behavioral and
subscription data.
- Performed exploratory data analysis (EDA) and feature engineering on customer dataset
- Trained a classification model using scikit-learn to predict churn probability
- Implemented data processing and database interaction with SQLAlchemy
- Built a FastAPI REST backend for serving model predictions
- Containerized the service using Docker
Tech: Python, Pandas, scikit-learn, FastAPI, SQLAlchemy, PostgreSQL, Docker

Air Raid Alert Prediction System (Team Project)
Led a team project focused on collecting public data and predicting the probability of air raid alerts
across Ukrainian regions.
- Organized and coordinated development as team lead
- Implemented data collection and preprocessing pipelines from multiple sources
- Trained machine learning models for regional alert probability prediction
- Developed a FastAPI backend for model inference
- Built a Streamlit dashboard for visualization and predictions
- Deployed the system on AWS EC2
Tech: Python, Pandas, NumPy, scikit-learn, FastAPI, Streamlit, AWS EC2, Docker

Food Vision — Image Classification (TensorFlow, CNNs, Transfer Learning)
- Multi-class food image classification system
- Data augmentation and transfer learning
- Full training and evaluation pipeline

Time Series Forecasting — Bitcoin Price Prediction
- Dense, CNN, LSTM, N-BEATS models
- Temporal windowing and proper train/test splits
- Evaluation with MAE and RMSE

EDUCATION
Bachelor of Applied Mathematics — National University of Kyiv-Mohyla Academy
Bachelor of Applied Mathematics — Kyiv School of Economics

Relevant coursework: Linear Algebra, Probability Theory, Mathematical Analysis,
Optimization Methods, Metric & Normed Spaces

CERTIFICATIONS
 Full list of certifications:
https://github.com/exEKS/CV

Advanced Deep Learning with TensorFlow Bootcamp
Complete SQL Mastery: Zero to Hero

Modern REST API Development with Flask & Python
AI Automation & LLM Applications: Building Intelligent Agents with n8n & APIs
Comprehensive Deep Learning Bootcamp with PyTorch

IBM | Developing AI Applications with Python and Flask (Coursera, 2025)
DeepLearning.AI & Stanford | Advanced Learning Algorithms (Coursera, 2025)
DeepLearning.AI & Stanford | Supervised Machine Learning: Regression & Classification (Coursera, 2025)