Developed data processing pipelines in Python using pandas and spaCy to clean and structure insights from over 1,000 global health reports. Analyzed maternal and child health datasets from WHO and NGO sources to identify regional vulnerability patterns through exploratory data analysis. Applied NLP and unsupervised learning techniques to extract actionable insights from unstructured health data, supporting research initiatives across multiple projects.

Optimized REST APIs (Node.js + Express) for user auth and content delivery — 35% faster average response time serving 10K+ daily requests. Redesigned MySQL schema with indexing and normalization — 40% faster queries on analytics endpoints. Deployed AWS EC2 + RDS backend stack with CloudWatch monitoring for API health.

Built a React + Node.js dashboard for 500+ customers, enabling self-service analytics and reducing ticket volume by 40%. Indexed 150K+ MongoDB documents → reduced dashboard load time 3.5s → 0.8s. Secured APIs with JWT authentication and bcrypt password hashing.

Developed spam detection model using TF-IDF + logistic regression — 85% precision / 82% recall on 3K+ customer tickets. Analyzed 200+ misclassified samples, introducing multilingual and short-query augmentation. Deployed classifier to production, reducing manual review workload by 30% weekly.