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.
Develop a library of reusable code that makes data scientists more productive across the organization.
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.
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.
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.
Write and document python code for data science (feature engineering and machine learning modeling) independently.
Develop and present summary presentations to business.
Act as peer reviewer of models.
Collaborate with peers to capture insights gained from data science studies.

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

machine learningdeep learningdata miningfeature engineeringstatistical reportingdata analysispythonmodel evaluationmodel optimizationdata science standards

Soft Skills

collaborationcommunicationsynthesis of complex informationtechnical explanationcoachingteachingpeer reviewinsight capturepresentation skillsproblem structuring