Projects are ordered for recruiter scanability: machine learning work first, then data product thinking, interactive study tooling, and evidence-based QA impact.
Project type: model validation case study. Skills demonstrated: Python, scikit-learn, model comparison, RMSE/MAE/R² evaluation, error interpretation, and production-risk analysis. Outcome: a clear validation review showing useful signal, model limitations, and what would be required before production use.
Project type: AI leadership and governance framework. Skills demonstrated: AI strategy, responsible AI, governance, risk assessment, data readiness, model validation thinking, automation planning, change management, and product decision-making. Outcome: a practical framework for evaluating where AI can create value, where it creates risk, and what controls are needed before deploying AI-enabled workflows.
Project type: data product case study. Skills demonstrated: data modeling, metadata architecture, source verification, dashboard design, product strategy. Outcome: structured pilot dataset covering works, publishers, translations, events, rights signals, and trust status.
Project type: professional impact summary. Skills demonstrated: defect analysis, test coverage, release readiness, Jira metric interpretation, automation contribution. Outcome: a detailed evidence page showing tracked QA delivery metrics, team context, and release-quality work.
Project type: static JavaScript learning tool. Skills demonstrated: DOM interaction, local storage, UX clarity, ML explanation, WPH-specific diagrams. Outcome: a study lab that explains ML concepts and how they apply to a data product.