Pacific Gas & Electric Company Principal Data Scientist, Strategic Data Science Team, Strategy & Analytics Division in San Francisco, California
Requisition ID # 12378
Job Category : Business Operations / Strategy; Energy Services / Trading; Information Technology
Job Level : Manager/Principal
Business Unit: Strategy and Policy
Job Location : San Francisco
Based in San Francisco, Pacific Gas and Electric Company, a subsidiary of PG&E Corporation (NYSE:PCG), is one of the largest combined natural gas and electric utilities in the United States.And we deliver some of the nation's cleanest energy to our customers in Northern and Central California. For PG&E, Together, Building a Better California is not just a slogan.It's the very core of our mission and the scale by which we measure our success. We know that the nearly 16 million people who do business with our company count on our more than 24,000 employees for far more than the delivery of utility services.They, along with every citizen of the state we call home, also expect PG&E to help improve their quality of life, the economic vitality of their communities, and the prospect for a better future fueled by clean, safe, reliable and affordable energy.
Pacific Gas and Electric Company is an Affirmative Action and Equal Employment Opportunity employer that actively pursues and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color,national origin, ancestry, sex, age, religion, physical or mental disability status, medical condition, protected veteran status, marital status, pregnancy, sexual orientation, gender, gender identity, gender expression, genetic informationor any other factor that is not related to the job.
The Strategic Data Science team utilizes advanced modeling and data science to drive PG&E’s transition to the sustainable grid of the future through quantitative decision-making. This work moves beyond descriptive reporting and is focused on driving the business forward through applied statistics, predictive and prescriptive analytics, and insightful tool design. The cornerstone of these high value analytics is one of the largest smart meter usage databases in the industry, that when combined with billing, program engagement, customer demographic, grid, and other data sources has unprecedented potential.
Past projects include:
Assessing the impact and viability of grid edge technologies and distributed energy resources
Forecasting market potential, and adoption of demand side management technologies, programs, and services
Optimizing non-wires alternative resource portfolio, location, and performance
Analyzing customer demographic, program participation and SmartMeter interval data to build program targeting propensity models
Supervised and unsupervised machine learning models using Python and Spark, executed on AWS
A selection of past presentations , awards , and papers are online. Additional examples available upon request
We are looking for a Principal Data Scientist to work on one or more of the areas mentioned above. The Strategic Data Science 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.
Excellent oral and written communication skills
Degree in computer science, engineering, applied sciences, mathematics, statistics, econometrics or similar quantitatively focused subject areas or job-related experience
Minimum of 10 years of relevant experience in data science or advanced analytics (through graduate education or work experience)
Demonstrated collaboration or paired development work history
Demonstrated proficiency and experience working with relational databases, preferably in SQL
Demonstrated proficiency writing clear and well documented code, preferably in Python
Demonstrated proficiency with data science best practices, such as version control via Git or similar
Demonstrated proficiency data visualization tools such as Tableau, D3, Plotly, etc.
Demonstrated proficiency with model development for decision analysis, forecasting, or other complex quantitative modeling
Demonstrated experience working with large datasets and knowledgeable about parallelization
Strong understanding of statistics and experience developing supervised & unsupervised learning models
History mentoring and teaching others as well as desire to continue to do so
Enjoy working on complex multi-stage projects with a diverse team
Involvement or strong interest in the energy/clean tech industry
History mentoring and teaching others
Familiarity with transmission and/or distribution power flow models
Past experience with advanced metering interval data
Experience in data engineering tools like Kafka
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.