Expert Data Scientist
 PG&E | |
Bay Area Maximum: $238,000
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 United States, California, Oakland | |
 Mar 18, 2025 | |
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Requisition ID# 164206 Job Category: Accounting / Finance Job Level: Individual Contributor Business Unit: Electric Engineering Work Type: Hybrid Job Location: Oakland Department Overview The Electric Risk Management (ERM) team plays a pivotal role in the evaluation, regulatory reporting, and investment planning process for PG&Es key and most consequential risks. The ERM team executes PG&Es risk decision framework that is used to support a data driven process to inform risk-based decisions with a focus on continuous improvement. This department partners with the Electric Organization to ensure responsibility for every aspect of PG&E's electric distribution and transmission operations, including planning, engineering maintenance and construction, asset management, business planning, restoration and emergency response. Position Summary This position will primarily lead the risk quantification efforts for all ERMs - enterprise risks. The successful candidate will be instrumental in executing the transition to a new risk-based evaluation that will provide more granularity and align PG&E's different risk models. This position will work closely with stakeholders within electric risk management (ERM), risk management and analytics (RADA), enterprise operations and risk management (EORM), electric system planning, transmission and distribution engineering, electric asset strategy, and asset knowledge management. In this role, you will quickly learn and apply our current risk framework. This includes bowtie development, evaluation of controls and mitigations, and support ad hoc risk analysis in support of our asset strategy partners. Excellent Excel auditing, implementation of Python codes, and Foundry code repository is required. This role will also require detailed understanding of our prioritization models and identifying the gaps with our current bowtie models that will need to be addressed and solved. In the transition to a new risk-based evaluation approach, the successful candidate will work closely with different stakeholders to incorporate requirements, plan and execute the new models. The prospective candidate is organized, technically oriented, can grasp concepts quickly, and adapt to new information. The successful candidate will be instrumental in managing the transition to a new risk-based evaluation that will provide more granularity and align PG&Es different risk models. This position is hybrid, working from your remote office and your assigned work location based on business need (Oakland). Currently, the team goes to the office once every two weeks or as required based on specific meetings and workshops. 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. A reasonable salary range is: Bay Area Minimum:$140,000 Bay Area Maximum: $238,000 This job is also eligible to participate in PG&E's discretionary incentive compensation programs. Position Duties (may include but are not limited to)-
Job Responsibilities * 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. * Acts as peer reviewer of complex models Qualifications- Minimum:
Desired:
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