Hassan Algoz is a data scientist and software engineer with 10 years' experience building production ML systems and scalable data pipelines across Saudi Arabia. He blends deep learning and classical ML expertise (PyTorch, scikit-learn, TF-IDF) with strong software engineering practices—modular architectures, testing, and CI/CD—to deliver reliable, explainable AI like multi-stage recommender systems and cost-optimized models that achieved 16x cost reduction and 9x speedups. Hassan has migrated and modernized systems at scale (Postgres→Cassandra/Elasticsearch, monolith→microservices with Go, gRPC, Kafka) and is comfortable operating Kubernetes-based microservice stacks. As an educator, he has delivered 1,000+ hours of training and mentored capstone projects, bringing an ability to translate complex ML concepts into practical solutions. He pairs hands-on experimentation in model selection and feature engineering with pragmatic tradeoff analysis, often leveraging prompt engineering and unsupervised techniques to squeeze more value from limited signals.
10 years of coding experience
2 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at King Fahd University of Petroleum & Minerals - KFUPM
Contributions:53 commits, 16 pushes, 1 branch in 18 days
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