Multilingual NLP System for Hazard Detection Developed a multilingual NLP system for detecting hazards and products from real-world food safety data. Processed noisy, imbalanced datasets with temporal and geographic normalization. Built features using TF-IDF, linguistic signals, and transformer embeddings. Evaluated models including Logistic Regression, SVM, XGBoost, and BERT-family architectures. Designed ensemble models optimized for competition metrics to improve generalization and robustness. Impact: Combined Macro-F1 = 0.81, strong performance across multilingual data.