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Telegram: DMan_Slav
LinkedIn: Dmytro Rybentsev
Dmytro Rybentsev
Position
Data Analyst

Summary of Data Analyst with a developer background. Proficient in SQL and Excel (including
Qualifications pivot tables), with hands-on experience in Python.
Highly motivated to drive data-driven product decisions through advanced A/B
testing, cohort analysis, and predictive ML modeling.

Technical Skills
- Power BI, GoogleSheets, Excel,
- Databases: SQL advanced,
Tableau + Prep, Statistics,
PostgreSQL, MySQL, MongoDB
LookerStudio, A/B tests, cohort
analysis, statistical testing, LTV

- Systems: Linux(Ubuntu),
- Python ecosystem (libraries):
Windows
numpy, scipy, matplotlib, seaborn,
hashlib, torch, torch-geometric, - Git
scikit-learn, transformers, jupyter,
sqlalchemy

Soft Skills
Stress Resistance and Adaptability: Cross-cultural Communication:

Proven ability to stay productive under Effective collaboration within diverse teams
pressure and in new environments. (experience in India, multicultural
environments).

Self-organization and Time Management: Social Responsibility:
Developed strong self-discipline and the Volunteer in orphanages; organized and
ability to manage my time effectively while conducted educational and recreational
living abroad, including organizing my daily events for children in orphanages and
routine, tasks, and priorities in new and boarding schools for vulnerable
dynamic environments. groups.

Positive Approach to Change and Travel: Leadership and Coordination:
 Comfortable with frequent travel and Experience in organizing myself and others,
adapting to new places, always looking for ensuring smooth collaboration in
opportunities to learn, grow, and enhance multicultural teams, and coordinating of
personal and professional experiences. actions

Experience in organizing festivals and
managing teams in dynamic environments.
Proven ability to coordinate actions in
unfamiliar settings.

Data Analyst
Experience / IT related
▪ SQL & Databases: Wrote complex SQL queries (SELECT, JOINs, subqueries, CTE, window
educational practice
functions, VIEWs, etc) to extract and manipulate data from relational databases.
▪ Data Visualization: Designed dashboards and reports using Tableau / Looker Studio
to track key metrics.
▪ Excel & Spreadsheets: Utilized advanced Google Sheets functions (Pivot Tables,
VLOOKUP, Filter) and Excel for preliminary data analysis and cleaning.
Exploratory Data Analysis (EDA): Conducted EDA to identify patterns, anomalies, and
correlations in datasets.

Systemic Risk Early Warning System (CascadeNet) | Advanced ML & Data
Analysis Project:
▪ Graph Machine Learning & Modeling: Developed "CascadeNet", a Multi-Task Graph
Neural Network (GAT) using PyTorch to simultaneously predict individual bank defaults
and systemic contagion sizes under macroeconomic stress.
▪ Data Engineering & Simulation Pipeline: Built an automated ETL pipeline to process
32 quarters (8 years) of interbank lending networks (AI4Risk dataset). Implemented the
Eisenberg-Noe clearing algorithm to parallelize the simulation of 6,400+ macroeconomic
stress scenarios.
▪ Advanced Analytics & Validation: Conducted extensive temporal performance analysis
proving the model's resilience during market regime shifts (COVID-19, 2023 banking
crisis). Demonstrated that the GNN architecture outperformed classical XGBoost
baselines in F1-score and probability calibration (Brier Score).
▪ Tech Stack: Python, PyTorch, PyTorch Geometric, Pandas, NetworkX, XGBoost, Scikit-
learn, Seaborn.

End-to-end A/B Testing & Product Analytics Pipeline
▪ ETL Pipeline: Developed ETL pipeline in Python to load and process 250,000+ user and
transaction records into PostgreSQL, ensuring data integrity and scalability for e-
commerce A/B testing.
▪ End-to-End Infrastructure: Built end-to-end analytics infrastructure for A/B test
evaluation (control vs. treatment groups), incorporating ETL (PostgreSQL), cohort
analysis, statistical testing (Z-test for conversions, Welch's t-test for revenue), and
interactive visualizations using Plotly.
▪ Analysis Modules: Implemented modules for cohort analysis (retention rates, LTV
calculations) and statistical hypothesis testing (Z-test, t-test) to assess experiment
effectiveness, enabling data-driven decisions on feature rollouts.

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 ETL Data Pipeline Project
▪ Automation: Developed an automated data pipeline using Python (Pandas) to extract
raw data from API, clean and transform it, and load it into a PostgreSQL Database .
▪ Data Quality: Implemented data validation checks to ensure accuracy before analysis.

Backend Development Project (Team of 6) Role: Backend Developer &
Database Designer
▪ Database Design: Designed the database schema and managed user permissions/roles
(relevant to "security" in FinTech).
▪ Log Analysis: Implemented and configured the ELK stack (ElasticSearch, Logstash,
Kibana) to collect, visualize, and analyze system logs for performance monitoring.
▪ Collaboration: Collaborated in an Agile environment, conducting code reviews and
managing tasks.

Spectral Geometry Research & Statistical Analysis
▪ Advanced Statistical Analysis: Conducted statistical analysis to evaluate network
properties, using stepwise regressions to prove exact correlations, achieving 𝑅^2 =
0.85 and 𝑝 = 4.2\𝑡𝑖𝑚𝑒𝑠10^{−7}.
▪ Robustness & Sensitivity Modules: Designed and implemented comprehensive stress
tests for various KNN parameters, boundary perturbations, and variations of random
initial values (seeds) to confirm the stability of the model

Links Link to the Systemic Risk Predictor repository: Cascade_net

Link to the Analytical Report with tables & visualizations: Report

Link to the End-to-end A/B Testing & Product Analytics Pipeline

Link to the Data pipeline(ETL) project: Data_pipeline

Link to my Tableau Public

Link to the repository with the project: Project-Stage-Academy/UA1244_alpha

Link to the Research
Languages
English Intermediate (B1)

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Education

Donbass State Machine-Building Academy 2008-2014

Additional education
(courses, trainings) Softserve Academy: Mate Academy:

- PYTHON FUNDAMENTALS - Data analyst course
- HTML5/ CSS3/ JAVASCRIPT FUNDAMENTALS
- DEVOPS FOR DEVELOPERS
- DATABASE FUNDAMENTALS
- PRACTICAL PYTHON
- PYTHON PROJECT-BASED LEARNING

Hobbies
Travels, Reading, Physics, Continuous Learning

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