Senior, Quantitative Analyst: Asset Failure Analysis, Electric Asset Strategy (Flexible location)

Pacific Gas And Electric Company•Oakland, CA

1d•$100,000 - $169,000•Hybrid

About The Position

The Senior Quantitative Analyst will apply advanced quantitative analytics techniques to support electric distribution’s advancement of asset insights, with a focus on identifying equipment failure causes, detecting adverse performance trends, and enabling data-driven insights and engineering-focused risk mitigation strategies. This role will support the Asset Failure Analysis (AFA) team by developing scalable, defensible, and reproducible analytical solutions using structured and unstructured data from across PG&E. The successful candidate will bring strong technical expertise and be comfortable working across a modern analytics stack, including Python, SQL, Palantir Foundry, and business intelligence tools. This role will also contribute to the adoption and implementation of artificial intelligence and machine learning solutions to enhance, streamline, and automate aspects of asset failure review, trend detection, and decision support. Possessing passion for data analytics and advancement of electric utility asset management concepts is critical. Your quantitative expertise will enable the Senior Data Scientist to quickly translate analytical insights into practical actions that will align with how electric distribution systems are designed, operated, and maintained. If you are seeking a challenging role that has a real impact in making California safer, come join our team.

Requirements

Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, Business or equivalent field.
Job-related experience: 4 years
OR Master’s Degree and 3 years of job-related experience
OR Doctorate and 1 year

Nice To Haves

Job-related experience: 6 years
Expertise in quantitative energy system modeling and analysis frameworks and techniques.
Knowledge of data science, machine learning, operations research, engineering, or economics analytical modeling tools and programming languages.
Experience at electric or natural gas utilities, independent power producers, wholesale energy market participants, regulatory agencies, consulting firms, or similar organizations
Demonstrated ability to take a conceptual issue and independently design and complete innovative analysis to draw meaningful conclusions.
Strong oral and written communication skills and ability to concisely convey information based on unique audience needs.
Capability in planning and prioritizing work to meet commitments aligned with organizational goals.

Responsibilities

Researches and applies knowledge of existing and emerging data science principles, theories, and techniques to inform engineering and business decisions related to electric distribution asset performance.
Creates data mining architectures, models, protocols, statistical reporting, and analytical methodologies to identify trends and failure patterns in structured and unstructured datasets.
Extracts, transforms, and loads data from dissimilar sources across PG&E to support feature engineering and analytical workflows.
Applies data science, machine learning, and artificial intelligence methods to develop defensible, reproducible predictive or optimization models supporting asset failure analysis and risk mitigation.
Partners with engineers, asset managers, compliance, inspections, wildfire risk and operations teams to ensure analytical outputs are actionable and aligned with electric distribution system design, operation, and maintenance.
Serves as the technical lead for the development of discrete analytical models and solutions, ensuring sound methodology and clear documentation.
Develops and presents complex analytical topics in a clear, concise manner to employees and stakeholders at various levels of the organization.