Senior Data Scientist

PG&E Corporation
Published
October 3, 2021
Location
San Francisco, CA
Category
Job Type

Description

Requisition ID # 117840 

Job Category : Accounting / Finance 

Job Level : Individual Contributor

Business Unit: Information Technology

Team Overview:

The Data & Analytics organization builds data products to inform PG&E’s Wildfire Safety Program.  The data science team specifically builds tools and models towards proactive care and maintenance of our electric assets. We collaborate closely with diverse partners across the company to prevent the wildfires in California, which are in part attributed to the failure of our utility equipment, and to inform public safety power shutoffs.

Our team employs a lean data-supported solution process to expand PG&E’s ability to mitigate the risk of wildfires through:

  • the development of computer vision models aimed at accelerating and automating asset inspection processes
  • the implementation of supervised and unsupervised machine learning models using Python to deploy on AWS
  • the interpretation and representation of meteorological data in models that combine a range data sources such as the electric system asset data, vegetation, and meteorology
  • the prediction of electric distribution equipment failure before it occurs allowing for proactive maintenance

Position Summary:

We are looking for a Senior Data Scientist to leverage the power of artificial intelligence (AI) technologies and our drone imagery database to develop and improve machine vision solutions which aid aerial inspection practices by bringing automation to the visual detection and categorization of damages and anomalies related to our electricity transmission and distribution equipment using image classification and object detection models.

In this position, you will have a unique opportunity to be at the forefront of utility industry analytics. You will work as a 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 advanced smart grid.

Responsibilities:

  • Develop, maintain, and improve computer vision models which are hosted on AWS for scaled use
  • Evaluate the performance of various Convolutional Neural Network (CNN) architectures and communicate the results to the business to demonstrate the value created through the deployment of such models
  • Appropriately document data sources, methodology, and evaluation metrics through using visual tools
  • Understand apply statistical techniques, analytical methods, and machine learning models such as classification, regression, clustering, anomaly detection, and neural networks to identify opportunities for operational improvement and develop strategic insight
  • Work closely with domain experts and develop a working knowledge of rate structures and elements, load shapes, and distributed energy resource technologies and policy options
  • Work with the leadership to continually improve analytics at PG&E via demonstrations, mentoring, and disseminating best practices

Required Qualifications:

  • Bachelor’s Degree in Computer Science, Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics or job-related discipline or equivalent experience
  • Job-related experience (e.g. data analytics and modeling), 5 years, OR Masters Degree and job-related experience, 3 years, OR Doctorate

Additional Qualifications:

  • Track record of writing clear and well documented code preferably in Python
  • Experience developing supervised and unsupervised learning models, neural networks (CNN, RNN, GCN, etc.)
  • Familiarity with deep learning frameworks (PyTorch, TensorFlow)
  • Excellent oral and written communication skills
  • Enjoy working on complex multi-stage projects with a diverse team
  • Involvement or strong interest in the energy industry
  • Expressed interest in learning, experimentation, and incorporation of new techniques
  • Distributed and cloud computing experience
  • Experience working with large datasets and knowledgeable about parallelization
  • Familiarity with transmission and distribution power flow models

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