Expert Data Scientist

Requisition ID# 169954

Job Category: Engineering / Science; Accounting / Finance; Business Operations / Strategy; Information Technology

Job Level: Individual Contributor

Business Unit: Electric Engineering

Work Type: Hybrid

Job Location: Oakland

Department Overview

Within the Electric Risk & Compliance organization, the Data Quality & Analytics team provides data analytics capabilities and platforms that enable effective responses to regulatory inquiries about customer affordability, system reliability, and public safety. The team works cross-functionally across the Electric Risk & Compliance organization to enable data driven decisions by applying industry-leading machine learning (ML), GenAI in alignment with PG&E's AI transformation. This work requires extracting useful insights from disparate data sets and facilitating actions informed by these insights.

Position Summary

We are seeking an experienced Data Scientist to help standardize and expand the data science platform capabilities of this team by capitalizing on enterprise resources and initiatives. Additionally, the Data Scientist will play a hands-on role in developing data science models that address important, timely regulatory topics for Electric Risk & Compliance. This includes contributing to thought leadership research within the Electric Risk & Compliance organization.

PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of thejob posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will bebased on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience,market value, geographic location, and internal equity.Although we estimatethe successful candidate hiredinto this rolewill be placed towards the middle or entry point of the range, the decisionwill be made on a case-by-casebasis related tothese factors.

A reasonable salary range is:

• Minimum Base Salary (Bay Area) $140,000.00
• Mid Base Salary (Bay Area) $189,000.00
• Maximum Base Salary (Bay Area) $238,000.00

Job Responsibilities Specific to the Role

Data Science Platform Improvement and Standardization

• Technology Evaluations: Contribute to design, implementation, and operation of an AI and deep learning model test bench. Conduct test bench studies and create reports of quantitative findings, recommendations
• MLOps and LLMOps: Develop a library of reusable code that makes data scientists more productive across the organization. This code will expedite data access and ETL/ELT workflows spanning multiple source systems across the Electric Risk & Compliance organization.
• Productivity: Collaborate with peers across the enterprise AI and Data Science communities at PG&E to assure the organization is capitalizing on enterprise initiatives and emerging technologies

Data Science Model Development

• Develop machine learning and deep learning models to investigate specific regulatory questions
• Scale and maintain these models as needed, including integration with AI agents in workflow settings
• Research and apply knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions
• Create data mining architectures / models /protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets
• Extract, transform, and load data from dissimilar sources from across PG&E for their machine learning feature engineering
• Apply data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models
• Co-develop mathematical models and AI simulations that represent complex business problems
• Write and document python code for data science (feature engineering and machine learning modeling) independently.
• Serve as the technical lead for the development of models.
• Develop and present summary presentations to business.
• Act as peer reviewer of models

Continuous Improvement

• Collaborate with peers to capture insights gained from data science studies.
• Speak internally and externally on AI; Provide thought leadership
• Build relationships across the company

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:

• Doctoral Degree or higher 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).
• Relevant industry (electric utility, renewable energy, analytics consulting, etc.) experience
• Demonstrated knowledge of and abilities with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them
• Competency in software engineering, statistics, and machine learning techniques as they apply to data science deployment
• Competency in commonly used data science and/or operations research programming languages, packages, and tools
• Hands-on and theoretical experience of data science/machine learning models and algorithms
• Ability to synthesize complex information into clear insights and translate those insights into decisions and actions. Demonstrated ability to explain in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
• Competency in the mathematical and statistical fields that underpin data science
• Mastery in systems thinking and structuring complex problems
• Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies