Principal, Data Scientist (Flexible location)

About the Role

Key Responsibilities

• Creates, applies, and evaluates advanced data mining architectures, models, protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets.
• Processes and analyzes large scale remote sensing and GIS datasets via advanced geospatial algorithms and models.
• Applies and evaluates data science, machine learning, and artificial intelligence methods to develop defensible and reproducible predictive or optimization models involving multiple facets and iterations.
• Writes and documents complex and reusable Python functions, as well as multi-modular Python code for data science.
• Provides thought leadership in the use of machine learning algorithms for solving business problems.
• Mentors junior data scientists and drives standardization in processes and toolsets across the data science community at PG&E.
• Collaborates with analytics platform owners to prioritize and develop scalable data science capabilities.
• Acts as a peer reviewer for complex models and AI algorithm proposals.
• Recognizes and prioritizes critical work related to data science models to maximize operational and strategic impact.
• Works with enterprise leaders to advocate for digital transformation through data science, analytics, and data-driven processes.
• Presents findings and makes recommendations to executive leadership and cross-functional management.

Requirements

• Bachelor's Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics, or an equivalent field.
• 8 years in data science (or 2 years if possessing a Doctoral Degree or higher in a related field).

Nice to Have

• Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
• Relevant industry experience (electric or gas utility, renewable energy, analytics consulting, etc.).
• Experience with geospatial algorithms and GIS data transformation and processing.
• Experience with Pyspark and geospatial libraries like Sedona and raster.