Open-sourced an end-to-end machine learning pipeline that predicts customer booking behaviour with 85% accuracy, plus a premium lounge demand forecasting framework.

Contributed a multi-model quantitative pricing framework for coffee futures options, combining Cost of Carry, Black-Scholes, and Monte Carlo simulation.

Improved ensemble model optimisation and added SHAP-based interpretability to surface the key drivers of customer churn.

Documented and refined the data analytics workflow for customer segmentation, sales trend analysis, and marketing experiment evaluation.

Expanded the analytics suite with telemetry-driven operational insights and a global pay equity evaluation dashboard.

Engineered and maintained 200+ repositories with 2000+ commits across data science, machine learning, NLP, computer vision, and AI projects.