Principal, Quantitative Analyst - Reliability, Strategy, and Partnerships

Requisition ID # 172444

Job Category: Business Operations / Strategy

Job Level: Manager/Principal

Business Unit: Strategy & Growth

Work Type: Hybrid

Job Location: Oakland

Department Overview

The System Performance, Reliability and Resiliency Strategy team within the overall Electric Transmission and Distribution Engineering organization is responsible for planning, organizing, and managing the resources necessary to successfully execute PG&E's Electric Reliability Strategy and initiatives. Within this department the Resiliency Strategy and Partnerships team will lead the long-term reliability and resiliency strategy. This work will include driving the development of related IEEE/IEC, FERC/NERC, EEI/IUNG/AEIC standards.

Position Summary

Serves as a recognized technical authority in applying advanced analytics-including data science, machine learning, operations research, engineering, and economic modeling-to support enterprise reliability and resilience strategy. Drives the development and application of sophisticated quantitative models and decision frameworks that inform long-term grid planning, reliability performance, risk management, and strategic investment decisions.

Establishes technical direction and best practices for advanced analytics in support of reliability engineering, ensuring scalable, high-quality solutions across the full analytics lifecycle, including data engineering, modeling, and visualization. Leads complex analytical efforts using large, diverse datasets to generate actionable insights that strengthen system reliability and resilience. Provides expert guidance to cross-functional teams and partners with senior leadership to integrate data-driven insights into enterprise strategy and decision-making.

Translates complex analytical findings into clear, executive-level recommendations that influence reliability outcomes, regulatory positioning, and long-term grid strategy. Builds and maintains strong relationships with internal stakeholders and external partners and represents the company in industry forums, contributing to best practices and thought leadership in reliability analytics and energy system modeling.

This position follows a hybrid work model, requiring employees to report to their assigned office location at least two or three days per week. The remaining days may be worked remotely, depending on business needs. The headquarters is located in the Oakland General Office.

PG&E is providing the salary range that the company, in good faith, believes it may pay for this position at the time of the job posting. This compensation range is specific to the job's locality. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, collective bargaining agreements, and internal equity. Although we estimate the successful candidate hired into this role will be placed toward the middle or entry point of the range, the decision will be made on a case-by-case basis, taking these factors into account. This job is also eligible to participate in PG&E's discretionary incentive compensation programs.

A reasonable salary range is:

Bay Area Minimum: $147,000

Bay Area Mid-Point: $199,000

Bay Area Maximum: $251,000

Job Responsibilities

• Manages and builds consensus for major cross-functional and cross-company projects and initiatives. Works with all levels of leadership (up to and including executives) across functions to influence decisions.
• Develops industry-wide best practices and works with other utilities and industry groups in the development of new and innovative data and quantitative models and research.
• Develops and evaluates advanced quantitative tools and examines alternative solutions to a problem, effectively troubleshoots various issues and analyses, and provides findings and recommendations for internal clients and external stakeholders.
• Proactively identifies future challenges and is responsible for innovating on existing models and analytical assumptions of the highest complexity.
• Coaches, mentors, and trains others.
• Influences policy and program strategy at other utilities and regulatory agencies.
• Sponsors testimony on complex analysis and policy issues.
• Provides critical and insightful assessments of third-party work products and comparisons to internal work products.
• Identifies and implements opportunities to improve business processes.
• Provides actionable input on the department's work planning, operations, and strategy.

Qualifications

Minimum:

• 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 8 years, OR Master's Degree and 7 years of job-related, OR Doctorate and 5 years.

Desired:

• Advanced Degree.
• Job-related experience 10 years.

Knowledge, Skills, Abilities, and Competencies:

• Expertise and thought leadership 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.
• Demonstrated ability to lead large and complex projects working with cross-functional teams.
• Excellent oral and written communication skills and ability to concisely and strategically convey information based on unique audience needs.
• Demonstrated capability in planning and prioritizing work to meet commitments aligned with organizational goals.
• Demonstrated integration of LEAN principles into work practices.