À propos
Sample activities include:
Development of new Machine Learning (ML) models characterizing and predicting distribution and transmission electric system environmental conditions using remote sensing data.
Development of end‑to‑end solutions that take satellite imagery and derived products and transform them into analysis‑ready insights, cloud‑optimised datasets accessible within distributed Spark environments.
Support for stakeholders in how to integrate remote‑sensing based features into downstream analysis and risk models.
Position Summary Designs, develops, and executes scripts, programs, models, algorithms, and processes, using structured, unstructured and geospatial data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine‑learning and artificial‑intelligence models (predictive or optimisation) for problem solving and strategy development. Participates in internal and external communities of practice in data science/artificial‑intelligence/machine‑learning to advance knowledge in the field. Educates the non‑technical community on advantages, risks, and maturity levels of data science solutions.
This position is hybrid, working from your remote office and your assigned work location based on business need. The assigned work location will be within the PG&E Service Territory.
Remuneration Bay Minimum: $140,000
Bay Maximum: $238,000
CA Minimum: $133,000
CA Maximum: $226,000
This job is also eligible to participate in PG&E's discretionary incentive compensation programs.
Job Responsibilities
Applies strong foundations of remote sensing and advanced techniques in image analysis, geospatial analytics, machine learning, and deep learning to extract insights from complex imagery datasets (e.g. multispectral, lidar, SAR).
Design and develop production‑quality spatial algorithms to extract, validate, and enrich features from remote sensing, derived imagery, and enterprise geospatial datasets.
Build and operate scalable, cloud‑native geospatial and ML pipelines for imagery ingestion, preprocessing, feature engineering, and delivery of production‑ready outputs across large spatiotemporal datasets.
Engineer and optimise cloud‑based geospatial and ML workflows using distributed processing frameworks to balance performance, cost, and reliability for batch and large‑scale workloads.
Develop and maintain reusable, well‑documented Python geospatial codebases and data catalogs, leveraging cloud‑optimised formats and STAC‑compliant architectures to ensure efficient access, processing, and reproducibility.
Research and apply advanced knowledge of existing and emerging geospatial data science principles, theories, and techniques to inform business decisions.
Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets.
Extracts, transforms, and loads data from dissimilar sources across PG&E for machine‑learning feature engineering.
Applies data science/ machine learning/ artificial intelligence methods to develop defensible and reproducible predictive or optimisation models that involve multiple facets and iterations in algorithm development.
Wrangles and prepares data as input of machine‑learning model development and feature engineering.
Writes and documents reusable Python functions and modular Python code for data science.
Assesses business implications associated with modelling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
Works with sponsor departments and company subject matter experts to understand application and potential of data science solutions that create value.
Plays findings and makes recommendations to senior management.
Act as peer reviewer of complex models.
Qualifications Minimum:
Bachelor's Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
6 years in data science
OR
no experience, if possess Doctoral Degree or higher, as described above.
Desired:
Doctorate Degree in Data Science, Machine Learning, or job‑related discipline or equivalent experience.
Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience.
4+ years of Python programming experience.
Proven experience and working knowledge of geospatial and remote sensing imagery data, with a focus on vegetation and environmental data.
Proven experience with methods and tools of geospatial data processing.
Experience writing software to extract features from remote sensing imagery data, time‑series, or large‑scale vector and raster datasets.
Familiarity with cloud computing platforms and distributed processing frameworks to support geospatial and remote sensing scalable analytics.
Experience with foundation models, machine learning or computer vision to transform imagery into intelligence.
Active participation in the external geospatial data science/artificial‑intelligence/machine‑learning community through publications, conferences, or open‑source contributions.
Competency with data science standards and best practices (model evaluation, feature engineering, reproducibility, deployment pipelines).
Competency with commonly used data science and/or operations research programming languages, packages, and tools for building data science/machine‑learning models and algorithms.
Proficiency in explaining complex technical concepts across geospatial analytics, machine learning, and data engineering to both technical and non‑technical audiences.
Strong communication skills and ability to mentor and develop others.
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; Corcoran; Cottonwood; Cupertino; Daly City; Danville; Davis; Dinuba; Downieville; Dublin; Emeryville; Eureka; Fairfield; Folsom; Fort Bragg; Fortuna; Fremont; French Camp; 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; 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 Jose; San Luis Obispo; San Mateo; San Rafael; 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; Watsonville; West Sacramento; Wheatland; Whitmore; Willits; Willow Creek; Willows; Windsor; Winters; Woodland; Yuba City
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Compétences linguistiques
- English
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