Data Scientist specialising in machine learning, model validation, and interpretable decision systems

I build and evaluate machine learning models using Python and SQL, with a focus on reliable performance, explainability, and real-world decision support. My work spans feature engineering, model validation, SHAP-based interpretation, and building interactive dashboards that translate data into actionable insight.

What I Deliver

Model Performance That Holds Up

Robust cross-validation, feature engineering, and careful metric selection to ensure models perform beyond test accuracy.

Explainable Insights for Stakeholders

SHAP-based interpretation and structured reporting that make model decisions understandable to non-technical teams.

Data to Decision

Dashboards and analytical outputs that translate predictions into practical business or operational action.