Data Scientist, Expert

PG&E; Oakland (CA); Hybrid; USD 140,000 - 238,000; Full time

Benefits offered by this job

Discretionary incentive program

Job summary

PG&E is seeking a data science professional to design, implement, and validate machine learning models using diverse data sources. The role focuses on predictive analytics and optimization to support wildfire prevention, emergency response, and operational decisions.

The position is hybrid, with a mix of remote work and Oakland-area office presence. Candidates should have deep ML fundamentals, strong Python skills, and experience handling large data sets in energy or utility environments.

Qualifications

• Bachelor's degree in a relevant field (Data Science, CS, Math, Econometrics, etc.).
• 6 years in data science OR Doctorate-equivalent experience as described.

Responsibilities

• Research and apply advanced data science principles to inform business decisions.
• Develop data mining architectures, models, and protocols for structured and unstructured data.
• Extract, transform, and load data from diverse sources for ML feature engineering.
• Develop defensible predictive or optimization models with multiple iterations.
• Prepare data and write reusable Python functions for ML pipelines.
• Assess modeling assumptions, inputs, and methodologies for business impact.
• Present findings to senior management and contribute to model reviews.

Skills

Data science

Python

Machine learning

SQL

Statistics

Education

Bachelor's degree

Tools

TensorFlow

Spark

PyTorch

Job description

Requisition ID# 174440

Job Category: Accounting / Finance

Job Level: Individual Contributor

Business Unit: Energy Delivery

Work Type: Hybrid

Job Location: Oakland

Department Overview

The Wildfire, Emergency and Operations (WEO) organization is responsible for oversight of PG&E's wildfire operations and associated mitigations. The organization is responsible for the development and maintenance of consistent processes and work standards associated with sustainable wildfire and emergency response preparedness operations in line with our regulatory policies and practices. Operational Safety, Enterprise Corrective Action Program, Safety Programs, Contractor Safety, and Transportation Safety & Compliance is also embedded within Wildfire, Emergency and Operations.

WEO partners with leaders in Energy Delivery and other parts of the business to develop and recommend a strategic direction for emergency preparedness, emergency response and public partnerships. Wildfire, Emergency and Operations is comprised of roughly 1,792 coworkers.

Position Summary

Designs, develops, and executes scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) 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 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.

Bay Minimum: $140,000
Bay Maximum: $238,000

This job is also eligible to participate in PG&E's discretionary incentive compensation programs.

Job Responsibilities

• Researches and applies advanced knowledge of existing and emerging 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 from across PG&E for their machine learning feature engineering
• Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization 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 modeling 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.
• Presents 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
• Experience in utility and energy industries
• Active participation in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through volunteering in professional organizations for the advancement of the field, presentations in conferences or publications to disseminate data science knowledge and topics, or similar activities.
• Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc) along with best practices to implement them
• Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities
• 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 in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
• Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders
• Mastery of the mathematical and statistical fields that underpin data science
• Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals