SDT_Muhammad Yusuf Rajabiyah

I've built automated data systems that turn operational documents and live data into reliable, usable insights. During my Data Scientist internship at PDAM Surya Sembada, I processed 288 billing documents through a backend automation system and reduced processing time from about five minutes per file to under 30 seconds.

I developed a PDF-to-PostgreSQL pipeline with duplicate-record validation based on billing period and customer ID, then integrated it with FastAPI and Docker Compose for internal use. The system saved an estimated 20+ hours of manual data entry work.

At PT. E-T-A Indonesia, I built a real-time Grafana monitoring dashboard and SQL pipelines to track On-Time Delivery performance and quantify losses from shipping delays. I also used preprocessing, feature engineering, clustering, and Silhouette Score evaluation to identify shipping-delay patterns and translate supply chain data into actionable insights.

I'm an Applied Data Science graduate building scalable data infrastructure, monitoring tools, and machine learning applications with Python, SQL, Spark, Kafka, FastAPI, Docker, and Linux.