Requisition ID

167320

Job Category

Accounting / Finance

Job Level

Individual Contributor

Business Unit

Electric Engineering

Work Type

Hybrid

Department Overview

The System Performance, Reliability and Resiliency Strategy team is dedicated to managing resources to successfully execute PG&E's Electric Reliability Strategy. Join a team of innovative individuals committed to deploying technology and modernizing the electric grid for safe and reliable operations.

Position Summary

As a pivotal member of the System Performance, Reliability and Resiliency Strategy team, you will report to the Senior Manager of Reliability Analytics. You will harness advanced data science models and cutting‑edge anomaly detection techniques to enhance the reliability of the electric transmission and distribution grid.

Key Responsibilities

• Lead the research and development of innovative methodologies to detect system failures and improve grid reliability.
• Apply data science, machine learning, and artificial intelligence methods to develop scalable and reproducible models.
• Serve as the technical lead in creating predictive and reliability analytics models.
• Develop python scripts for data processing and model development (e.g., ML/AI models).
• Document processes, datasets, and results to ensure transparency and reproducibility.
• Contribute to data science strategies that align with team goals.
• Communicate technical concepts and model results effectively to stakeholders.

Qualifications

Minimum

• Bachelor's Degree in a relevant field such as Data Science, Machine Learning, or Engineering.
• 4 years of experience in data science or 2 years with a Master's Degree.

Desired

• Ph.D. or Master's degree in a related field.
• Experience in the electric or gas utility, renewable energy, or analytics consulting sectors.
• Strong foundation in statistics, machine learning (ML), and artificial intelligence (AI).
• Hands‑on experience in deploying data science models using Python.
• Proven ability to tackle complex, unstructured problems with data‑driven approaches.
• Proficiency with large datasets, both structured and unstructured.
• Excellent communication skills for explaining technical concepts to non‑technical audiences.
• A knack for mentoring and coaching career‑level data scientists in AI/ML techniques.