Primary focus
ML QA Engineer · Data Quality Engineer
Roles where QA discipline, Python automation, data validation, model evaluation, and production risk thinking are directly useful.
Open to ML QA · Data Quality · QA Automation
Tetiana Kravchuk
Software QA Engineer → Data Science & ML Quality
9+ years in software QA · MS in Data Science Candidate, Boston University — Expected May 2026 · Massachusetts-based · Open to remote/hybrid roles
I bring a QA mindset to data and machine learning systems: validating workflows, finding risk early, checking assumptions, and turning technical evidence into decisions teams can trust.
Primary focus: ML QA Engineer · Data Quality Engineer. Secondary focus: QA Automation Engineer with Python · Data Analyst · Junior Data Scientist.
Focused on ML QA and data quality roles.
My strongest fit is at the intersection of software QA, data validation, automation, and machine learning model evaluation.
Secondary focus
QA Automation Engineer with Python · Data Analyst · Junior Data Scientist
- QA Automation Engineer with Python
- Data Analyst
- Junior Data Scientist
Recruiter Snapshot
A quick scan of the experience, credentials, and role fit behind the portfolio.
Experience
9+ years Software QA experience with manual, regression, mobile, release, and automation work.
Education
MS in Data Science Candidate, Boston University — Expected May 2026.
Technical stack
Python, SQL, pandas, scikit-learn, QA automation, Appium, Sauce Labs, test planning, and model evaluation.
Validation focus
QA evidence, data quality checks, model comparison, release readiness, and production-risk analysis.
Target roles
Primary focus: ML QA Engineer and Data Quality Engineer. Secondary focus: QA Automation Engineer with Python, Data Analyst, and Junior Data Scientist.
Location
Remote/hybrid preference, Massachusetts and Boston-area teams.
Technical Stack
Core tools and methods I use across QA automation, data validation, model evaluation, and applied data science work.
Languages
Python · SQL
Data Science
pandas · NumPy · scikit-learn · model evaluation · regression · classification · feature engineering
QA & Automation
manual testing · regression testing · test planning · test case design · Appium · Sauce Labs · Pytest · Selenium familiarity
Data / ML Quality
data validation · residual analysis · baseline comparison · drift monitoring · golden datasets · model cards
Tools
Git · GitHub · Jupyter Notebook · VS Code · Jira · Confluence
Languages
English · Ukrainian · Russian
Multilingual background supports international data validation, metadata review, and cross-language product QA.
Featured work
Projects selected to show technical depth, business thinking, and the bridge from software quality to data science.
Machine learning capstone
ML Model Validation Case Study: Residential Property Values
A model validation case study showing how I evaluate performance, compare algorithms, identify limitations, and reason about production risk, fairness, and data quality.
Publishing intelligence dataset
World Publishing Houses Dataset
A structured portfolio dataset for books, publishers, translations, verification status, market events, and rights signals across the Nordic publishing ecosystem.
Interactive learning product
Data Science Flashcard Lab
A static JS study lab for ML concepts, QA-for-ML thinking, and World Publishing Houses examples, with local storage for custom cards.
Professional impact
QA Metrics & Automation Impact
Evidence-based QA story using bug reporting, ticket closure, test case creation, release support, automation contributions, and triage improvements.
Working principle
Reliable AI and data products start with trustworthy inputs, clear assumptions, tested workflows, and honest evaluation. My QA background gives me a practical lens for building ML systems that are not only accurate in notebooks, but understandable and testable in real product conditions.