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Grant Vermillion has extensive work experience in the fields of data science, machine learning, and computational physics. Grant currently holds a position at Strong Analytics as a Senior Machine Learning Scientist, where they have been working since September 2022. Before that, Grant worked as a Machine Learning Scientist at the same company from January 2022 to September 2022.

Prior to their role at Strong Analytics, Grant worked as a Data Scientist at Transport Foundry from February 2020 to December 2021.

Grant has also gained research experience during their time at various institutions. Grant served as a Research Fellow at the Max-Planck Institute for Solid State Research in Stuttgart, Germany from July 2018 to February 2020. Grant'sresearch focused on investigating nanoconfined water in beryl using Density Functional Theory techniques.

Additionally, Grant worked as a Research Assistant at the Institute for Computational Physics at the University of Stuttgart from June 2019 to December 2019. During this time, they developed a Python library called Quantum Valet (QV) that automates tasks related to building training sets for machine learning force fields.

Grant's research work started as a Fulbright Research Fellow at the Institute for Computational Physics at Universität Stuttgart from September 2017 to July 2018. Grant conducted research on the dynamics of single water molecules within nanocages of the beryl crystal, employing both molecular dynamics and ab initio calculations.

Earlier in their career, Grant held multiple roles at Linfield College, including Physics Department Tutor and Lab Teaching Assistant from February 2015 to May 2017. Grant also worked as an Undergraduate Researcher, where they developed a computational model of fly swarms using MATLAB.

Furthermore, Grant was part of the REU (Research Experience for Undergraduates) Cohort at Howard University in May 2016. Grant conducted density functional theory (DFT) calculations on zinc oxide (ZnO) nanostructures to explore their potential for functionalizing graphene as a biosensor.

Throughout their work experience, Grant has gained expertise in machine learning, data analysis, computational physics, and research methodologies.

Grant Vermillion started their education journey at Chemeketa Community College in 2013, where they pursued a degree in Physics. Grant attended this institution until 2014, after which they transferred to Linfield University. At Linfield, Grant continued their studies in Physics and Mathematics, and they successfully completed their Bachelor of Science degree in 2017. After obtaining their undergraduate degree, they pursued further education at the University of Stuttgart from 2017 to 2019. During their time at the University of Stuttgart, Grant specialized in Physics and earned a Master of Science degree.