About

Software QA Engineer transitioning into Data Science and ML-focused quality roles.

I bring 9+ years of QA experience, MS in Data Science candidacy at Boston University, and evidence-based delivery metrics to ML QA and data quality roles.

MS in Data Science Candidate, Boston University — Expected May 2026

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TK
Open to remote and hybrid opportunities in Massachusetts / Boston-area teams.Primary focus: ML QA Engineer · Data Quality Engineer. Secondary focus: QA Automation Engineer with Python · Data Analyst · Junior Data Scientist.EmailLinkedInGitHubResume PDF

My path

I began my career in software quality, where I learned to think in edge cases, user flows, risk, evidence, and release readiness. Over 9+ years, I have worked across manual QA, automation, mobile testing, regression planning, test case design, release validation, defect analysis, and cross-functional delivery.

That foundation now shapes how I approach data science. I care about the full system: data collection, assumptions, model behavior, evaluation metrics, user impact, and what happens after a model leaves the notebook.

Current focus

Primary focus: ML QA Engineer · Data Quality Engineer.

Secondary focus: QA Automation Engineer with Python · Data Analyst · Junior Data Scientist.

My strongest fit is at the intersection of software QA, data validation, automation, and machine learning model evaluation.

I am especially interested in healthcare, biotech, regulated data, publishing metadata, and systems where trust, provenance, and careful validation matter.

Recruiter Snapshot

Compact proof points for QA-to-data-science transition roles.

Experience

9+ years Software QA experience across manual, regression, mobile, feature, and release workflows.

Education

MS in Data Science Candidate, Boston University — Expected May 2026.

Focus

ML QA, data quality, Python automation, model evaluation, and production-readiness thinking.

Languages

English · Ukrainian · Russian.

Multilingual background supports international data validation, metadata review, and cross-language product QA.

Project proof

Portfolio work that connects QA thinking with applied data science and product reliability.

ML Model Validation

Residential property value modeling reframed around validation, error interpretation, limitations, and production risk.

View ML Case Study

World Publishing Houses

A publishing intelligence data product concept focused on metadata trust, verification status, and translation transparency.

View Case Study

Flashcard Lab

A static JavaScript study tool for ML concepts, QA-for-ML, and WPH applications.

Open Study Lab

My Working Style

Calm, structured, evidence-based, and practical.

Clarity before complexity

I prefer clear goals, documented priorities, testable acceptance criteria, and honest tradeoffs.

Quality as a system

I look beyond individual bugs to patterns: missing requirements, brittle flows, unclear data, poor handoffs, and weak observability.

Responsible ML mindset

I evaluate models with attention to data quality, bias, error behavior, monitoring, and business context.