Software QA Engineer transitioning into Data Science and ML-focused quality roles, with 9+ years of QA experience, MS in Data Science candidacy at Boston University, and strong evidence-based delivery metrics.
MS in Data Science Candidate, Boston University — Expected May 2026
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Software QA Engineer and Data Science graduate student with 9+ years of experience in software testing, automation, regression strategy, release validation, and defect analysis. Strong background in Python-based QA automation and growing specialization in applied machine learning, data quality, ML QA, and production-minded data products.
My strongest fit is at the intersection of software QA, data validation, automation, and machine learning model evaluation.
QA Automation Engineer with Python · Data Analyst · Junior Data Scientist
Full resume available as PDF. Company name available on request.
Current employer — wealth-management / financial technology SaaS platform
Current role
QA automation, Appium, Sauce Labs, test planning, regression testing, mobile testing, API testing, release validation, and defect triage.
Python, SQL, pandas, NumPy, scikit-learn, model evaluation, data validation, regression, classification, clustering, and error analysis.
Selenium — familiar, continuing practice. TensorFlow/PyTorch — academic and project-based learning. Azure, GCP, and Databricks — coursework and applied learning context.
Boston University
MS in Data Science Candidate — Expected May 2026
English · Ukrainian · Russian.
Multilingual background supports international data validation, metadata review, and cross-language product QA.
For a deeper view of my work, start with the ML model validation case study and the World Publishing Houses data product case study.