Summary
Daniel Amaral is a Data Scientist and Ph.D. candidate in Teleinformatics Engineering at the Federal University of Ceará with eight years of experience applying machine learning and deep learning to real-world problems. He combines a strong statistical foundation (BSc in Statistics) and a Master's focus on pattern recognition to build predictive models for IoT, beekeeping, and public-sector projects. Formerly part of the Sm@rtbee research initiative, he now contributes to public innovation as a data scientist at IRIS | Laboratório de Inovação e Dados on the Chief Scientist project. Proficient in R and Python and comfortable with a bit of web development, he blends research rigor with production-minded engineering. Known for turning complex signal and pattern-recognition problems into actionable insights, he thrives at the intersection of academic research and applied government innovation.
8 years of coding experience
PhD in Teleinformatics Engineering, Communication Systems and Networks, PhD in Teleinformatics Engineering, Communication Systems and Networks at Universidade Federal do Ceará