Abdulla Muaz

Abdulla Muaz is a Data Scientist with over 12 years of experience, specializing in combining software engineering and data science to deliver high-impact solutions, particularly in industries that rely on weather forecasts and in-situ measurements. His core expertise lies in applying AI and machine learning to real-world scenarios, driving value for meteorological services and other data-driven industries.

Muaz holds a Master of Science in Data Science and Business Analytics from Asia Pacific University of Technology & Innovation and a Bachelor of Science in Information Technology and Software Engineering from Middlesex University, UK. His professional journey includes a lead role at the Maldives Meteorological Service, where he spearheaded the development of integrated climate and meteorological decision support systems.

Currently, as a Data Scientist at TempoQuest, Muaz is focused on developing cutting-edge tools, such as the Forecast Verification Tool (FVT) , designed to enhance weather forecast verification processes. He is also leading key projects aimed at leveraging AI/ML techniques to improve decision-making across various sectors and is proficient in cloud computing, deep learning, and automation tools such as Docker and AWS.

Muaz's technical proficiency spans Python, SQL, TensorFlow, PyTorch, and cloud services, making him a key asset in implementing AI/ML-driven solutions for complex meteorological and climate challenges.