Назарій

Nazarii Okhrimchuk
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Professional Summary
Enthusiastic and analytical Junior Data Scientist with a strong foundation in data
analysis, machine learning, and statistical modeling. Proficient in Python, SQL, and
version control with Git. Experienced in handling and visualizing datasets using
libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Missingno. Passionate
about extracting insights from data to drive decision-making.

Technical Skills
• Programming Languages: Python
• Libraries & Frameworks: NumPy, Pandas, Scikit-learn, Seaborn, Matplotlib,
TensorFlow, Keras, Missingno, StatsModels, Fuzz
• Data Analysis & Machine Learning: Data cleaning, exploratory data analysis
(EDA), regression models, classification models, feature engineering, neural
networks with TensorFlow/Keras
• Version Control: Git, GitHub
• Databases: PostgreSQL
• Web Scraping: BeautifulSoup
• Tools & Environments: Jupyter Notebook, VS Code

Education
Aplied Math, Taras Shenchenko National University of Kyiv
09, 2023 – 06, 2027
Relevant coursework: Probability and Statistics, Machine Learning, Database
Management, Data Visualization

Projects
Survivality Prediction

• Developed a machine learning model using Scikit-learn to predict survivality
on the RMS Titanic.
• Performed data cleaning and feature engineering using Pandas and Missingno.
• Visualized key insights using Matplotlib and Seaborn.
Fraud prediction

• Developed a machine learning model using Scikit-learn to predict card fraud.
• Performed data cleaning and feature engineering using Pandas and Missingno.
• Visualized key insights using Matplotlib and Seaborn.

Harmful posts classification

• Developed a machine learning model to automatically detect whether a
Telegram post contains harmful
• Collected and annotated Telegram post data using custom scraping tools and
manual labeling.
• Evaluated model performance using cross-validation and metrics such as
precision, recall, and F1-score.

Experience
Freelance Data Parsing Specialist

Freelance (2 Months)

• Scraped and extracted data from websites using Python and BeautifulSoup.
• Processed and structured extracted data for analysis.
• Automated data collection pipelines for continuous updates.

Junior Data Scientist

Hamster Agro Innovation

• Assisted in building data pipelines to collect and preprocess agricultural data
from sensors, drones, and field reports.
• Participated in exploratory data analysis (EDA) to identify trends in crop yield,
soil quality, and weather impact.

Additional Information
• Languages: English, Ukrainian
• Interests: Data Science, Machine Learning, AI Research