I’ve worked extensively with structured business data sources such as Salesforce, SharePoint, internal databases, and email systems, developing classification, clustering, and predictive models to support data-driven decision-making. My focus has been on Gradient Boosting, XGBoost, and LightGBM models, enhanced with calibration techniques and SHAP-based explainability for transparent and reliable predictions.
I’ve also built FastAPI-based prediction pipelines to operationalise machine learning models, incorporating comprehensive feature engineering, encoding methods, and skewness correction. On the unsupervised side, I’ve designed clustering systems using PCA and Yeo-Johnson transformations, evaluating performance through Silhouette, Davies–Bouldin, and Calinski–Harabasz indices to uncover meaningful customer or opportunity segments.
I have also built a full-stack predictive maintenance pipeline project, which predicts machine failure before it occurs and suggests a planned maintenance procedure to reduce downtime and prevent critical machine breakdown
I’m passionate about connecting data, automation, and business strategy, creating scalable, interpretable, and high-impact machine learning systems that drive measurable results.
Please find my CV attached below
Note: I have a transferable Iqama