Duncan Campbell is a data scientist with 12 years of quantitative research and applied analytics experience, currently focused on maritime analytics and LNG optimization. With a PhD in Astronomy & Astrophysics from Yale and a research-to-product pathway through roles at Carnegie Mellon, Epistemix, and Nautilus Labs, he blends deep scientific rigor with real-world operational impact. At Danelec as Principal Data Scientist he applies probabilistic modeling and domain-aware ML to improve vessel efficiency and fuel decisions. His background in academic teaching and interdisciplinary training in philosophy and astronomy gives him a knack for explaining complex models to nontechnical stakeholders. Known for translating high-dimensional research into robust production pipelines, he moves projects from prototype to measurable cost and emissions reductions. Based in Pittsburgh, he brings a rare combination of astrophysical modeling pedigree and hands-on maritime industry experience.
12 years of coding experience
3 years of employment as a software developer
Bachelor's Degree, (B.S.) Physics, Astronomy & Astrophysics, Bachelor's Degree, (B.S.) Physics, Astronomy & Astrophysics at University of Michigan
Doctor of Philosophy (Ph.D.), Astronomy and Astrophysics, Doctor of Philosophy (Ph.D.), Astronomy and Astrophysics at Yale University
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