Kelvin Clark
Independent Contractor at Chevron
Richmond, Virginia, United States
Summary
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Rockstar🎓
Top SchoolKelvin Clark is an independent contractor and contracts/procurement specialist with over 14 years of international experience delivering material management, supply chain and procurement solutions for onshore oil and gas projects. He has led tendering, bid evaluation and award processes for major operators and service companies—coordinating F.A.T., expediting, logistics and vendor pre-qualification across complex EPCC programs. Kelvin pairs hands-on field coordination for fracturing and wellhead equipment with strategic procurement planning, having managed acquisitions from mobile cranes to subsea casings and specialist software like Petrel. His background includes senior stakeholder engagement with multinationals such as Chevron, Shell and Halliburton, and a track record of improving supplier performance and procurement controls. Notably, he also contributed as an ML engineer to the pretraining pipeline of the influential ELECTRA project, showing technical versatility beyond procurement. Based in Richmond, VA, he combines operational pragmatism with cross-cultural leadership to drive timely, compliant project delivery.
13 years of coding experience
6 years of employment as a software developer
Master's degree, Petroleum Technology/Technician, Master's degree, Petroleum Technology/Technician at California University of Management and Sciences
English
Github Skills (5)
nlp10
deep-learning10
tensorflow10
python10
machine-learning9
Programming languages (4)
Github contributions (5)
google-research/electra
Mar 2020 - Mar 2021
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 6 PRs, 17 pushes in 1 year 1 month
Contributions summary:Kelvin primarily contributed to the pre-training process of the ELECTRA model. Their work involved modifying scripts related to building and running the pre-training dataset, including bug fixes and the addition of features. They addressed issues in dataset generation, incorporated code to compute FLOPs, and made minor fixes to the pretraining configuration. This user focused on the core functionality of the ELECTRA pre-training process.
nlptransformer-architecturebertlanguage-modelingdeep-learning
clarkkev/attention-analysis
Jun 2019 - Jan 2020
Contributions:10 commits, 2 PRs, 9 pushes in 7 months
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