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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data sciencemachine learningdeep learningETLELTfeature engineeringstatistical reportingdata miningpredictive modelingoptimization

Soft Skills

collaborationthought leadershiprelationship buildingcommunicationinsight synthesistechnical explanationcoachingteachingcontinuous improvementpeer review