Hybrid Data Scientist

at Pacific Gas and Electric Company

• Job title: Data Scientist
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $140,000 - $238,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: ETL, Python
• Location requirements: Hybrid, Oakland, United States

About the role

• Experienced Data Scientist developing machine learning models for regulatory compliance at PG&E. Collaborating across teams to standardize data science practices and enhance analytics.

Responsibilities

• 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

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, as described above).
• 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
• 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.