Samuel Hinton is a Senior Data Scientist with 12 years of experience blending software engineering, machine learning and astrophysics to solve high-impact, real-world problems. He builds production ML pipelines and MLOps for energy markets and battery asset optimisation, while previously creating classifiers and large-scale simulations in a PhD in Astrophysics. Comfortable across the stack, he has a software engineering foundation from enterprise BI and web apps through to scalable data engineering and automated model deployment. He also teaches applied statistics and Python data workflows, translating complex methodologies into clear, reproducible courses and materials. Samuel’s work habit—writing code daily since his teens—means he pairs rigorous Bayesian modelling and simulation experience with pragmatic, production-focused delivery. Based in Queensland, Australia, he brings a rare mix of astrophysical modelling depth and commercial data-science impact.
12 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Astrophysics Bayesian Modelling Machine Learning, Doctor of Philosophy - PhD Astrophysics Bayesian Modelling Machine Learning at The University of Queensland
Contributions:3 releases, 2447 commits, 5 PRs in 1 year 11 months
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