Особисті дані приховані

Dmytro Chumakov
Data Analyst
Chisinau, Moldova • [відкрити контакти](див. вище в блоці «контактна інформація») • [відкрити контакти](див. вище в блоці «контактна інформація») • [відкрити контакти](див. вище в блоці «контактна інформація») •
LinkedIn • @dmytrochumakov - telegram

SUMMARY
Data Science graduate (UTM, 2026) with practical experience in data analysis, machine learning, and SQL. Final -
year thesis project involves designing a context-aware recommendation system that integrates meteorological
data (UTCI index) with ML algorithms (Random Forest, scikit-learn) to generate personalized activity suggestions.
Additional hands-on experience f rom a QA internship, where data documentation and structured analysis were
core responsibilities. Looking f or an entry-level Data Analyst or Junior Data Scientist role.

PROJECTS
Weather-Based Leisure Recommendation System
Bachelor's Thesis • Technical University of Moldova • 2025–2026
• Designed a context-aware recommender system (CARS) that maps real-time weather data to personalized
leisure activity suggestions based on individual user pref erences
• Implemented UTCI (Universal Thermal Climate Index) calculation pipeline using OpenWeather API data –
integrating temperature, humidity, wind speed, and mean radiant temperature
• Built and compared ML classif ication models (Random Forest, SVM, AdaBoost) using scikit -learn to predict
user activity comf ort scores across 10 thermal stress categories
• Designed a relational PostgreSQL database schema with normalized tables f or users, activities, weather logs,
and pref erence weights – supporting a dynamic f eedback learning loop
• Developed the backend API using FastAPI (Python)
• Conducted a comparative analysis of 6 thermal comf ort indices and 5+ existing leisure/weather platf orms to
justif y the UTCI-based approach

DomaFlow – SaaS Product Analytics & QA
CyberWhale SRL • Chisinau, Moldova • 2026 • 1 month internship
• Created a structured Tracking Plan with 60+ user events covering the f ull product lif ecycle – def ining event
names, triggers, properties, and business priorities
• Built a User Journey Map identif ying f riction points, user attitudes, and opportunities across key product f lows
(property management, document assembly, CRM)
• Documented 75+ test cases and 20+ bugs with structured reproduction steps and severity classif ication,
maintaining accuracy across a complex multi-module SaaS product

SKILLS
Data & Analytics Technical Stack
Python: pandas, scikit-learn, matplotlib, seaborn Backend: Python, FastAPI, Docker
Machine Learning: Random Forest, SVM, AdaBoost, Frontend: Next.js, TypeScript, JavaScript
regression, classif ication Other: Git, Notion, HTML, CSS
Databases: SQL, PostgreSQL, MySQL
Languages
Data processing: KDD pipeline, normalization, f eature
engineering English: B2
APIs: OpenWeather API, REST (FastAPI) Russian: Native
Ukrainian: Native
Product & Analytics Tools
Romanian: A1
Tracking Plan: event taxonomy, triggers, properties
User Journey Map: pain points, behavior f lows
Testing & QA: Jira, Postman, TestRail
EDUCATION
B.Sc. Data Science – Technical University of Moldova (UTM)
Chisinau, Moldova • 2023 – 2026 • Final year
• Coursework: machine learning, databases (SQL), statistics, data analysis, sof tware engineering
• Thesis: Weather-Based Leisure Recommendation System using UTCI index and ML classif ication
Fundamentals in Programming – Samsung IT School
2021 – 2022