Data Scientist, Expert

Department Overview

The Wildfire Risk Organization is responsible for assisting the company to act decisively and transparently to prevent fires of consequence from being caused by our equipment. The organization will develop objectives to
1) prevent fires of consequence originating from our equipment;
2) meet all commitments outlined in our Wildfire Mitigation Plan;
3) continue to foster trusted relationships with key stakeholders; and
4) develop consistent processes and work standards through the implementation of the Lean Operating System for sustainable operations.

Designs, develops, and executes scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Participates in internal and external communities of practice in data science/artificial intelligence/machine learning to advance knowledge in the field.

Educates the non-technical community on advantages, risks, and maturity levels of data science solutions.

This position is hybrid, working from your remote office and your assigned work location based on business need. The assigned work location will be within the PG&E Service Territory. Position will report to Oakland and San Ramon as needed.

PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity.

Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case‑by‑case basis related to these factors.

Bay Minimum: $140,000

Bay Maximum: $238,000

CA Minimum: $133,000

CA Maximum: $226,000

This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.

• Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
• Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets
• Extracts, transforms, and loads data from dissimilar sources from across PG&E for their machine learning feature engineering
• Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models that involve multiple facets and iterations in algorithm development.
• Wrangles and prepares data as input of machine learning model development and feature engineering
• Writes and documents reusable python functions and modular python code for data science.
• Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
• Works with sponsor departments and company subject matter experts to understand application and potential of data science solutions that create value.
• Presents findings and makes recommendations to senior management.
• Act as peer reviewer of complex models

• 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

• Doctorate Degree in Data Science, Machine Learning, or job‑related discipline or equivalent experience
• Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience
• Experience in designing and implementing quantitative models to estimate wildfire risk,…