Олена

OLENA HUZENKO
DATA ANALYST | MACHINE LEARNING | QUANTITATIVE ANALYTICS
Kyiv, Ukraine
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PROFESSIONAL PROFILE

Data & QA professional with 3+ years of experience in technical R&D environments, combining
quality assurance, data analysis and machine learning evaluation. Hands-on experience with
Python, R and machine learning through professional projects and an MSc in Digital Business &
Data Analytics.
Professional experience includes analysing technical data and system behaviour, evaluating ML
model outputs, investigating errors and inconsistencies, and working with engineers to identify and
resolve technical issues. More recently, gained dedicated Data Analyst experience working with
real-world datasets and Python-based data processing workflows.
Strong academic and practical background in supervised and unsupervised machine learning,
anomaly detection, clustering, classification and model evaluation. Looking to apply this
combination of technical, analytical and ML experience to quantitative analysis and financial risk
projects.

CORE COMPETENCIES

Programming & Data
● Python — Pandas, NumPy, Jupyter
● R —statistical analysis, data modelling, probability-based reasoning
● SQL — basic; actively developing
● SAS Enterprise Miner — basic
● Data cleaning, preprocessing and transformation
● Exploratory data analysis
● Data quality validation
● Reproducible analysis workflows

Machine Learning & Statistics
● Supervised & unsupervised learning
● Classification and regression
● Clustering — KMeans, HDBSCAN
● Autoencoders
● Anomaly detection
● PCA and dimensionality reduction
● Model evaluation and comparison
● Precision, Recall, F1, ROC-AUC, PR-AUC
● Experimental design and parameter tuning
● Statistical analysis and hypothesis testing

Additional Technical Skills
● TensorFlow / Keras
● scikit-learn
● Power BI
● Excel
● Git — basic
● Command line / Unix — basic
● Computer vision / image-based ML — practical exposure
PROFESSIONAL EXPERIENCE

DATA ANALYST
T-Spark | Ukraine, remote/part-time
September 2025–November 2025; February 2026–June 2026

● Worked directly with technical leads to clarify data requirements, validate assumptions and
communicate analytical findings.
● Built Python-based data cleaning and preprocessing workflows for real-world sensor datasets
containing approximately 259K rows.
● Handled missing values, inconsistent formats and corrupted records to prepare reliable
datasets for further analysis.
● Prepared reproducible datasets and analytical outputs in CSV and JSON formats for technical
review and modelling.
● Performed data quality checks and identified inconsistencies affecting downstream analysis.
● Produced structured analytical reports and diagnostics to support engineering decisions and
further development.
● Worked with technical teams to investigate data-related issues and clarify next analytical steps.

CANYON DEVELOPMENT | Ukraine October 2021–July 2023

QA TEAM LEAD | August 2022–July 2023
QA MANAGER | October 2021–August 2022

QA Team Lead

● Led and coached a QA team of 11 engineers, coordinating testing, analytical evaluation and
delivery activities.
● Supported validation and release readiness across 10+ hardware and software R&D
projects.
● Delivered validation and analytical reports for 50+ technical deliverables.
● Coordinated QA and engineering activities, prioritising work according to project risks and
delivery requirements.
● Performed manual evaluation of computer vision ML models for object recognition, including
prediction correctness, consistency checks and failure-case analysis.
● Analysed misclassification patterns across test runs and documented model weaknesses to
support further improvement.
● Supported preparation and validation of image datasets for model retraining.
● Tested ML models for heart and lung sound analysis, evaluating output stability and
sensitivity to input variations.
● Introduced structured testing and verification workflows that improved consistency and reduced
rework.
● Communicated analytical findings, risks and recommendations to technical stakeholders.

QA Manager

● Designed evaluation and test plans based on system behaviour, identified risks and defined
measurable acceptance criteria.
● Worked with engineers to define ML model evaluation scenarios and performance criteria.
● Performed manual testing and validation of computer vision models for object recognition.
● Investigated model failures and analysed patterns in incorrect predictions and inconsistent
outputs.
● Built structured verification workflows and reporting templates to ensure repeatable and
traceable evaluation.
● Worked with technical leads to investigate root causes and define corrective actions.
● Evaluated ML outputs for medical audio data, including heart and lung sound analysis.

QA ENGINEER
Otis Tarda | Ukraine August 2020–October 2021

● Investigated technical issues using structured testing and data analysis.
● Coordinated corrective actions with production and engineering teams.
● Managed multiple technical issues simultaneously, prioritising cases according to production and
delivery requirements.
● Maintained technical records, inspection documentation and traceability in an ISO-controlled
environment.

CERTIFICATIONS

Data Science Job Simulation — Forage
Certificate of Completion | December 2025
● Completed practical data science tasks involving eligibility logic and customer behaviour
prediction.

SELECTED DATA SCIENCE PROJECTS
MSc Thesis — Unsupervised Anomaly Detection in Time-Series
Python | TensorFlow/Keras | HDBSCAN | KMeans | PCA
● Designed an unsupervised ML pipeline for detecting contextual and collective anomalies in
unlabeled time-series data.
● Built an encoder–decoder LSTM autoencoder for anomaly detection.
● Compared clustering approaches including KMeans and HDBSCAN.
● Performed parameter tuning and structured experimental evaluation.
● Evaluated model performance using Precision, Recall, F1, PR-AUC and ROC-AUC.
● Analysed model behaviour and trade-offs between anomaly detection performance and
interpretability.

Airbnb Market Segmentation
R | Clustering | Decision Trees | Power BI
● Analysed 96K+ London Airbnb listings to identify pricing and host behaviour patterns.
● Applied clustering and decision-tree modelling to segment the market.
● Presented analytical findings through a Power BI dashboard for non-technical audiences.

Student Satisfaction Modelling
R | PCA | ANOVA | Logistic Regression
● Applied PCA to identify the key dimensions influencing student satisfaction.
● Used statistical comparisons and logistic regression to analyse relationships between variables.
● Interpreted model results to support data-driven conclusions.

Bank Marketing Analytics
R | Random Forest | Logistic Regression
● Built classification models to predict customer response to marketing campaigns.
● Compared modelling approaches and identified important predictive features.
● Used model outputs to support customer segmentation and targeting.
EDUCATION

UNIVERSITY OF READING, HENLEY BUSINESS SCHOOL
United Kingdom | 2024–2025
Master of Science, “Digital Business and Data Analytics”
Relevant areas: Business Analytics, Business Intelligence, Data Mining (statistical modelling,
probability), Econometrics fundamentals, Project Management, AI & Data Analytics, Business
Requirements Analysis.
OLES HONCHAR DNIPRO NATIONAL UNIVERSITY
Ukraine | 2018–2021
Bachelor’s Degree, “Management”
UKRAINIAN STATE UNIVERSITY OF CHEMICAL TECHNOLOGY
Ukraine | 2017–2020
Bachelor’s Degree, “Chemical Technology and Engineering”

ADDITIONAL INFORMATION

● Ukrainian — native; English — B2.