The Enterprise Data Science & Artificial Intelligence (EDS&AI) department is both a “Delivery” team that is a sophisticated practitioner of data science and a “Center of Excellence” team that supports other practitioners in an enterprise-wide Hub & Spoke analytics adoption model.

As a Delivery team, this Department uses industry leading data science, artificial intelligence (AI), machine learning (ML) and Generative AI (GenAI) technologies to drive the company's transition to the sustainable grid of the future. EDS&AI works cross-functionally across the company to enable data driven decisions applying advanced analytics & AI techniques, as well as improvements to relevant business processes. Deployed to some of the company's highest priority arenas, EDS&AI does not specialize in a traditional utility domain, such as asset management or program administration, but instead specializes in extracting useful insights from disparate data sets and facilitating actions informed by these insights.

As a Center of Excellence in DS&AI team, this Department listens to the needs of AI/ML/GenAI and data science practitioners across the company, along with emerging industry practices, and builds standards, processes, tools, knowledge, and best practices that meet the current and future needs of the enterprise.This team works on a wide variety of difficult problems, offering great variety in the work, and constant opportunity to explore and learn. Current and past engagements include:

- Creating wildfire risk models that are used by regulators and the utility to prioritize asset management

- Developing computer vision models that improve, accelerate, and automate asset inspections processes

- Predicting electric distribution equipment failure before it occurs, allowing for proactive maintenance

- Forming the analytical framework behind the company's Transmission Public Safety Power Shutoff

- Optimizing non-wires alternative resource portfolios, like the Oakland Clean Energy Initiative, including location and resource adequacy considerations

- Analyzing customer demographic, program participation, and SmartMeter interval data to build program targeted propensity models, e.g., for customer owned distributed energy resource technologies

- Identifying and investigating anomalous customer natural gas usage, in order to resolve dangerous customer side leaks Position Summary