Summary
Majid Aldosari is a Data Scientist with 13 years of engineering experience and six years focused on professional data science, specializing in time series analysis and productionizing ML workflows. He bridges domain knowledge to deployable data products by integrating problem framing, advanced statistical and deep learning models, and robust infrastructure (Kubernetes, Terraform, Azure, dbt). At PNNL he accelerated team productivity and exploratory analysis through generalizable tooling for messy, large CSV datasets, Bayesian uncertainty modeling, and semantic graph mapping between design tools and digital twins. Earlier roles include automating distributed TensorFlow training/serving and low-level data format translations at Kinetica, and Bayesian/GP models for building operations at Enertiv—showing a consistent focus on making sophisticated analytics auditable and iterable. Trained in mechanical and computational engineering, he brings a rare combination of high-performance simulation background and pragmatic software engineering to data science projects.
12 years of coding experience
12 years of employment as a software developer
Bachelor of Science - BS, Mechanical Engineering, Economics, Bachelor of Science - BS, Mechanical Engineering, Economics at Vanderbilt University
Master of Science (M.S.), Computational Science (and Informatics), Master of Science (M.S.), Computational Science (and Informatics) at George Mason University
Arabic, English