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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. 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.
Job Responsibility:
Contribute to design, implementation, and operation of an AI and deep learning model test bench
Develop a library of reusable code that makes data scientists more productive
Collaborate with peers across the enterprise AI and Data Science communities
Develop machine learning and deep learning models to investigate specific regulatory questions
Scale and maintain these models as needed
Research and apply knowledge of existing and emerging data science principles
Create data mining architectures / models /protocols, statistical reporting, and data analysis methodologies
Extract, transform, and load data from dissimilar sources
Apply data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models
Co-develop mathematical models and AI simulations
Write and document python code for data science independently
Serve as the technical lead for the development of models
Develop and present summary presentations to business
Act as peer reviewer of models
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
Requirements:
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)
Demonstrated knowledge of and abilities with data science standards and processes (model evaluation, optimization, feature engineering, etc.)
Competency in software engineering, statistics, and machine learning techniques
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
Competency in the mathematical and statistical fields that underpin data science
Mastery in systems thinking and structuring complex problems
Nice to have:
Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field
Relevant industry (electric utility, renewable energy, analytics consulting, etc.) experience
Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies