Principal, Quantitative Analyst: Asset Failure Analysis, Electric Asset Strategy (Flexible location)
- Location
- Oakland, CA
- Work model
- On-Site
- Level
- Principal
- Posted
Skills
About this role
Requisition ID # 175205 Job Category: Business Operations / Strategy Job Level: Manager/Principal Business Unit: Energy Delivery Work Type: Hybrid Job Location: Oakland; Alameda; Alta; American Canyon; Angels Camp; Antioch; Auberry; Auburn; Avenal; Avila Beach; Bakersfield; Balch Camp; Bay Point; Bear Valley; Belden; Bellota; Belmont; Benicia; Berkeley; Brentwood; Brisbane; Buellton; Burney; Buttonwillow; Calistoga; Campbell; Canyon Dam; Canyondam; Capitola; Caruthers; Chico; Clearlake; Clovis; Coalinga; Colusa; Concord; Concord; Corcoran; Cotati; Cottonwood; Cupertino; Daly City; Danville; Davis; Dinuba; Downieville; Dublin; Emeryville; Eureka; Fairfield; Folsom; Fort Bragg; Fortuna; Fremont; French Camp; Fresno; Fresno; Fulton; Garberville; Geyserville; Gilroy; Goodyear; Grass Valley; Guerneville; Half Moon Bay; Hayward; Hinkley; Hollister; Holt; Houston; Huron; Jackson; Kerman; King City; Lakeport; Lemoore; Lincoln; Linden; Livermore; Lodi; Loomis; Los Banos; Lower Lake; Madera; Magalia; Manteca; Manton; Mariposa; Martell; Marysville; Maxwell; Menlo Park; Merced; Meridian; Millbrae; Milpitas; Modesto; Monterey; Montgomery Creek; Morgan Hill; Morro Bay; Moss Landing; Mountain View; Napa; Needles; Newark; Newman; Novato; Oakdale; Oakhurst; Oakley; Olema; Orinda; Orland; Oroville; Palo Alto; Palo Cedro; Paradise; Parkwood; Paso Robles; Petaluma; Pioneer; Pismo Beach; Pittsburg; Placerville; Pleasant Hill; Pleasanton; Point Arena; Potter Valley; Quincy; Rancho Cordova; Red Bluff; Redding; Richmond; Ridgecrest; Rio Vista; Rocklin; Roseville; Round Mountain; Sacramento; Salida; Salinas; San Bruno; San Carlos; San Francisco; San Francisco; San Jose; San Luis Obispo; San Mateo; San Rafael; San Ramon; San Ramon; Sanger; Santa Cruz; Santa Maria; Santa Nella; Santa Rosa; Selma; Shaver Lake; Sonoma; Sonora; South San Francisco; Springville; Stockton; Storrie; Taft; Tracy; Turlock; Twain; Ukiah; Vacaville; Vallejo; Walnut Creek; Wasco; Washington; Watsonville; West Sacramento; Wheatland; Whitmore; Willits; Willow Creek; Willows; Windsor; Winters; Woodland; Yuba City Department Overview Welcome to Electric Asset Strategy (EAS). We are responsible for the Distribution electric system’s asset strategy, standards, procedures and end-to-end risk management for 2.3 million structures. We deliver a risk informed strategic plan, leveraging inspection insights and asset data to drive strategic maintenance and investment decisions. The Distribution Asset Risk Management (ARM) team—comprising Asset Failure Analysis (AFA), Asset Insights, and Inspections & Maintenance Strategy (I&M)—focuses on continuously improving on how PG&E monitors and maintains its assets to support a resilient, reliable grid to help keep our communities safe. Position Summary The Principal 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