Requisition ID # 92402
Job Category : Accounting / Finance
Job Level : Individual Contributor
Business Unit: Wildfire Risk
Based in San Francisco, Pacific Gas and Electric Company is one of the largest combined natural gas and electric utilities in the United States (NYSE:PCG). In addition to providing energy to approximately 40% of Californians and 1 in 20 Americans, PG&E also delivers some of the nation’s cleanest electricity, nearly 80% from GHG emissions free sources, and 0% from coal. As an active member of the community PG&E aims to improve our customers’ quality of life, economic vitality, and prospect for a better future by providing clean, safe, reliable and affordable energy. As such, PG&E proactively advocates for regulation of greenhouse gases through partnerships (such as at the UN COP in Paris), invests in renewables, and supports customer affordability through one of the country's most successful energy-efficiency programs. More information on PG&E and its other innovative sustainability initiatives can be found at http://www.pge.com/about
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 2021 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 going into 2022 and beyond.
The aim of the Risk Analytics team is to enhance the risk practices of PG&E’s Electric Operation business and thereby address changing external conditions such as climate change. To this end the Risk Analytics team creates and maintains tools to enable PG&E to close the gap between metrics and electric system performance. These tools provide a multi-layered view of risk across the electric system so that decision-making processes include and empower employees at all levels of the company to manage risk appropriately.
In creating these tools, the team employs a data supported, lean solution process to expand PG&E’s ability to assess and manage risk. The result are assessments and mitigations that are more dynamic, quantitative, and customer-focused, with a multi-layered approach for both short-term and long-term time horizons.
Sample activities include:
- Interpretation and representation of meteorological data in models that combine a range data sources such as the electric system asset data, vegetation and meteorology
- Development of computer vision models aimed at accelerating and automating asset inspections processes
- Predicting electric distribution equipment failure before it occurs allowing for proactive maintenance
- Supervised and unsupervised machine learning models using Python and Spark, executed on AWS
We are looking for a Senior Data Scientist to work on one or more of the areas mentioned above. The Risk Analytics team is looking to expand on its data science capabilities -- you will have a unique opportunity to be at the forefront of utility industry analytics. In this role you will work as part of cross functional teams, including other data scientists, technology experts, and subject matter experts to develop data driven solutions. It is the perfect role for someone who would like to continue to build upon their professional experience and gain a comprehensive view of the nation’s most advance smart grid.
Work Location: Flexible Within California
Analytics and Modeling
- Gather, prepare, and analyze data from disparate sources to produce user-friendly models and actionable insights
- Work closely with domain experts. Develop a working knowledge of rate structures and elements, load shapes, distributed energy resource technologies and policy options
- Develop expertise with grid data, customer demographic information and other structured data sets
- Understand and apply statistical and analytical modeling methods such as classification, regression, clustering, anomaly detection, neural networks, etc. to identify opportunities for operational improvements and develop strategic insights
Communication, Summary Presentation and Visualization
- Appropriately document data sources, methodology, and model evaluation metrics
- Develop and present summary presentations for senior management
- Create streamlined visual tools for end-users
- Work with business partners to advance business processes, based on analytical findings
- Work with team leadership to continually improve analytics at PG&E via demonstrations, mentoring, disseminating best practices, etc.
- Degree in computer science, engineering, applied sciences, mathematics, statistics, econometrics or similar quantitatively focused subject areas or job-related experience
- Minimum of 5 years of relevant experience in data science or advanced analytics (through graduate education or work experience)
- Excellent oral and written communication skills
- Demonstrated collaboration or paired development work history
- SQL proficiency and experience working with relational databases
- Track record of writing clear and well documented code, preferably in Python
- Strong understanding of statistics and experience developing supervised & unsupervised learning models
- Demonstrated experience with data science best practices, such as version control via Git or similar
- Demonstrated experience with data visualization tools such as Tableau, D3, Plotly, etc.
- Demonstrated experience with model development for decision analysis, forecasting, or other complex quantitative modeling
- Enjoy working on complex multi-stage projects with a diverse team
- Involvement or strong interest in the energy/clean tech industry
- Expressed interest in learning, experimentation, and incorporation of new techniques
- Distributed and cloud-based computing experience
- Experience working with large datasets and knowledgeable about parallelization
- Familiarity with transmission and/or distribution power flow models
- Past experience with advanced metering interval data
- Experience in data engineering tools like Kafka
- GIS experience