Risk Engineer

Requisition ID # 172731 Job Category: Engineering / Science Job Level: Individual Contributor Business Unit: Strategy & Growth Work Type: Onsite Job Location: Danville; San Ramon Department Overview Electric Transmission & Distribution ensures the delivery of clean, safe, reliable and affordable energy to nearly 16 million people in Northern and Central California. The Electric Transmission & Distribution Team is responsible for ensuring the electric grid's reliability and resiliency through the ongoing maintenance and operations of PG&E's 100,000 miles of distribution lines and over 18,000 miles of Transmission lines. Coworkers in Electric Transmission & Distribution actively engage in the Lean Methodology through the use of visual management in operating reviews to cross-functionally identify and solve problems, and create standard work. By working together as One Electric. One Team. We are delivering for our hometowns. Electric Transmission & Distribution is comprised of approximately 6,500 coworkers. Position Summary Provides engineering, testing and analysis services and expertise across multiple disciplines to various company organizations to identify, diagnose and solve technical problems. Provides consultation to clients to proactively avoid future problems. Conducts inspections, failure analysis, performance assessments, and evaluations on a wide variety of systems and components. Partners with management to create bid proposals for requested client work.This position will have a focus on risk modeling and management. This position is hybrid, working from your remote office and your assigned work location based on business need. PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. 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, and internal equity. Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors.​ A reasonable salary range is:​ Bay Area Minimum: $​109,000 Bay Area Maximum: $​175,000 Job Responsibilities Risk modeling Collaborates with peers to prepare technical reports and specifications and to identify, define, and solve problems. (both) Continually identifies and escalates testing or process improvements. Participates in identifying innovations and cost-reduction potential of new and existing technologies. Independently performs routine engineering analysis and performs lead role in determining scope, schedule, budget and resources needed. Prepares cost and materials estimates for use in client bid proposals. Assists senior peers on more complex tests and projects, participating in planning, budgeting and testing. Completes multiple projects concurrently. Prepares tests and studies. Evaluates performance of parts, assemblies or systems under simulated operating conditions. Records results and makes recommendations based on findings. Develops test protocols, models, test technologies and analyzes data. May assist in the development of technical policies and procedures. Modifies prototypes, parts, assemblies, and systems to correct functional deviations. Begins to develop knowledge in other areas of specialty/discipline or continues to focus on one area and increases depth of knowledge May interface with clients regarding status of project or to collect more information. Ensures client expectations for project are met. Qualifications Minimum: Bachelor’s degree in Engineering with emphasis in a discipline related to assignments. (i.e., mechanical, civil or electrical engineering, etc.) Job-related experience, 3 years Desired: Masters Degree or equivalent experience PE-Professional Engineer license Travel required; frequency depends on assignment (10% -75%). Strong expertise in modeling, statistical and machine learning approaches