David works within an internal AI consultancy, spearheading the end-to-end design and implementation of apps and pipelines incorporating AI. He uses his petroleum engineering roots to meet with experts from the business and turn their needs into programmable inputs and outputs.
David is accomplished in the predictive maintenance, subsurface, and retail fuels pricing domains. He has deployed applications, predictive systems, and helped write policy regarding use of AI in energy.
Currently David uses python, azure, aws, and docker to frame the needs of Shell into something executable that delivers value. He is certified in apache airflow and has a background in big data and electricity generation.
Creating a centralized emissions data model can enhance transparency and comparability, but involves significant implementation challenges including data standardization, quality, migration, and CAPEX investment. In this session our panelists will debate if the benefits really outweigh the challenges.