Mohamed Wael Mohamed

I’m an Applied AI & Data Analytics trainee through Egypt’s Digital Pioneers Initiative, combining an Agricultural Engineering background with a strong foundation in statistics, data mining, and machine learning. I focus on turning data into measurable improvements, from feature engineering to model evaluation.

Across hands-on work, I’ve delivered two production-grade ML systems with accuracy gains, including an 82% logistic regression cancellation predictor trained on 119,390 hotel bookings and deployed behind a FastAPI REST endpoint. I bring rigorous EDA, hypothesis testing, ANOVA, and correlation analysis to validate features before modeling.

I also build modern ML solutions across domains: a multitask Arabic NLP routing model using fine-tuned CAMeL-BERT (HuggingFace Transformers) for ministry, severity, and spam detection, and a custom CNN for multi-scale weapon threat detection with PyTorch, evaluated via mAP and FPS benchmarking.

I’m comfortable with end-to-end workflows and deployment—using Azure/AWS, Docker, and CI/CD—along with MLOps tooling like WandB and MLflow for tracking and monitoring. My goal is to contribute practical applied machine learning while continuously improving reliability, performance, and business impact.