David Schaub

Senior AI Engineer Shell

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.

Agenda Day 1 - October 8, 2025

10:10 AM (4:10PM CET) Centralized Emissions Data: A Feasibility Check

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.

While using AI and ML can help standardize diverse operational data sets, considering the implementation issues, do they really improve the efficiency of data migration?
Limited and conflicting data hinders the ability to measure emissions reduction impact. Does a centralized view provide the insights needed, or risk overlooking key areas?
Data-agnostic solutions will undoubtedly streamline the process of converting raw data into actionable insights, thereby boosting efficiency and profitability. But can these benefits be achieved when working on a facility-by-facility basis?

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