Ольга

Olha Ivakhnenko
Lisbon, Portugal
[відкрити контакти](див. вище в блоці «контактна інформація»)
[відкрити контакти](див. вище в блоці «контактна інформація»)
Data Analyst/ Scientist https://www.linkedin.com
/in/olga-ivahnenko/

SUMMARY SKILLS / COMPETENCIES

Passionate about analyzing complex datasets to uncover actionable insights.
Expertise includes visualisation, statistical modeling (linear/nonlinear regression, Tools and Technologies:
classification, clustering, segmentation), time series analysis, and predictive
modeling. Python ( Pandas, NumPy,
SciPy), PySpark, R
Skilled in crafting impactful data visualizations and presenting insights to drive SQL
informed decision-making. Tableau, PowerBI
Statistica, EViews, Vensim

PROJECTS
LANGUAGES
1. Crypto Game Tokenomics Verification Simulation

Role: data scientist. English B2
Portugal A2
Methodology and Techniques: system dynamics models, simulation modeling. Ukrainian, Russian Native
Tools and Technologies: Vensim, Python, PySD.
Project is a crypto game that offers a unique blend of gameplay, earning, and
real-life quests in the improvement and defense of ecology. EXPERIENCE
Responsibilities:
October 2023 - November
Developed a comprehensive simulation model to assess and validate the 2024
tokenomics of the project, ensuring robustness and viability within the in-game WormFare
economy. Data scientist

2. Distribution of state budget funds between private and state providers of 2011 - present
healthcare services in Ukraine: the consequences of reforming the sphere in Simon Kuznets Kharkiv
the conditions of the challenges of war National University of
Role: data scientist/ analyst, scientific researcher. Economics
Associate Professor
Methodology and Techniques: exploratory data analysis, unsupervised ML, cluster
analysis, predictive analytics, and time-series forecasting, visualisations. Research center for
Tools and Technologies: Python, Tableau. industrial problems of
development of National
The project was carried out under a grant and brought the second place award at academy of sciences of
the competition organized by the U.S. National Academy of Sciences, Simon Ukraine
Foundation at the Faculty of Applied Sciences of the UCU. Senior Researcher
It aimed to analyze the allocation of public funds in Ukraine's healthcare. Сluster
analysis was utilized to identify regional medical hubs. The constructed predictive
models enabled determining future medical service distribution between private
and municipal providers. EDUCATION
Responsibilities:
Ph.D. in Economics,
Strategizing the research approach, defining methodologies, and outlining the specialization:
steps for comprehensive analysis within the subject area. Conducting data analysis Mathematical methods,
using Python to identify trends in the allocation of public funds in the healthcare models, and information
sector. Analyzing data for insights and identifying imbalances. Developing technologies in economics
predictive models to determine the future development of medical services Simon Kuznets Kharkiv
between private and municipal providers, forecasting changes in the occurrence National University of
of strokes and heart attacks following the ongoing war. Economics, 2010
Applying cluster analysis methods to identify regional medical hubs and pinpoint

key centers for the provision of medical services. Master's degree in
Creating visual models and graphs to demonstrate changes in service distribution Economic Cybernetics
across regions, types of medical service providers, and service packages. Simon Kuznets Kharkiv
National University of
3. Power BI Training for HR Analytics Economics, 2005
Role: trainer, business analyst
Methodology and Techniques: instructional design, HR metrics and KPI analysis CERTIFICATIONS &
COURSES
Tools and Technologies: Power BI, DAX, Power Query, Excel.
The project was aimed at developing HR specialists’ skills in building interactive SQL Fundamentals
dashboards for HR Analutics. The training focused on key HR metrics, workforce skill track in DataCamp
dynamics, and visualization techniques to support strategic HR management. October 2025
Responsibilities:
Data Analyst in Tableau,
Designed a structured training program on Power BI tailored for HR analytics use career track in DataCamp
cases. Conducted interactive workshops demonstrating dashboard creation, DAX Issued Apr 2025
calculations, and data visualization best practices. Guided participants in analyzing
workforce data (e.g., turnover, recruitment, employee engagement). Provided Data Analysis in Tableau,
practical exercises and case studies to strengthen data interpretation and CERGE-EI Foundation
decision-support skills. Issued Apr 2025

4. Analysis of Patterns for Long-Term Success in the Music Industry Introduction to Python
Role: data analyst programming for big data
and data science
Methodology and Techniques: exploratory data analysis, hierarchical clustering
Kharkiv IT Cluster
algorithms, K-mean and variations, PCA.
Issued May 2023
Tools and Technologies: R, ggplot2, dplyr, Python, Matplotlib.
SQL for Data Science
The project was aimed at the identification of key behavioral patterns associated
CourseraCoursera
with long-term success in the music industry. Employed various clustering
Issued Nov 2022
algorithms and feature space-narrowing methods to analyze raw data. Developed
data-driven insights and recommendations to reduce investment risks for artists
Data visualization
with limited growth potential.
Prometheus
Responsibilities: Issued Jan 2018
Applied unsupervised machine learning techniques, including hierarchical
Data analysis and
clustering, to identify distinct behavioral patterns among successful artists.
statistical inference in the
Utilized modified versions of the K-means, particularly K-medoids algorithms, to R language Prometheus
further refine and validate the identified patterns. Collaborated with Issued Nov 2016
cross-functional teams to integrate research findings into business strategies and
decision-making processes.

5. Models for Assessing Heterogeneity and Cyclical Dynamics in the
Socio-Economic Development of Regions
Role: data scientist
Methodology and Techniques: exploratory data analysis, various cluster analysis
techniques, time series regression models, and simulation models based on the
system dynamics concept.
Tools and Technologies: R, Statistica, Vensim.
Large international project based on a multinational grant. An ambitious goal was
set to build a concept for assessing the degree of heterogeneity in the
development of regions from the point of view of the danger to the security of the
state. Within the framework of this concept, an assessment of the state of
heterogeneity of territories was carried out, and a set of simulation models for the
territories’ development based on predictive analytics was developed.
Responsibilities:
managed all the research processes and was fully involved in them, from the
business understanding of the problem, data understanding, and preparation to
building and teaching models and models deployment (more than 100 models,
mainly time series regression and simulation, also cluster analysis and a lot of
descriptive statistics and data visualization).