About

QA experience. Data science direction. Product-minded execution.

I’m a Massachusetts-based Software QA Engineer and Data Science graduate student focused on QA Automation, Data Quality, ML QA, and reliable data products.

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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

I am completing a Master’s in Data Science at Boston University and building a portfolio around practical ML, data quality, AI testing, and product analytics. My goal is to work in roles where software reliability and data science meet: QA Automation/SDET, Data Quality, ML QA, AI evaluation, MLOps-adjacent QA, and applied data science.

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

How I work

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.