Data Scientist, Expert

PG&E Corporation
Published
April 16, 2021
Location
San Ramon, CA
Category
Job Type

Description

Requisition ID # 92404 

Job Category : Accounting / Finance 

Job Level : Individual Contributor

Business Unit: Wildfire Risk

Company Overview

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.

Team Overview

The aim of the Risk and Data 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 and Data 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 executed on AWS

Position Summary

We are looking for an Expert Data Scientist to join our growing team. In this role you will have a unique opportunity to be at the forefront of utility industry analytics and their use in tools to asses risk. Working as part of cross functional team, including other data scientists, technology experts, and subject matter experts this individual will help develop data driven solutions for decision making and operations. 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.

Responsibilities:

Analytics and Modeling

  • Gather, prepare, and analyze data from disparate sources to produce user-friendly models and actionable insights
  • Understand and appropriately 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
  • Work collaboratively with other data scientist through an iterative Agile project development lifecycle

Communication, Summary Presentation, and User Interfaces

  • Appropriately document data sources, methodology, and model evaluation metrics
  • Develop and present summary presentations to management
  • Create streamlined visuals, and tools for end-users  

Minimum Qualifications:

  • Degree in computer science, engineering, applied sciences, mathematics, statistics, econometrics or similar quantitatively focused subject areas or job-related experience
  • Minimum of 8 years of relevant experience in data science or advanced analytics OR Master’s Degree and job-related experience, 6 years, OR Doctorate and job-related experience, 3 years

Desired Qualifications:

  • Strong oral and written communication skills
  • Demonstrated collaboration or paired development work history
  • Demonstrated proficiency with relational databases, preferably in SQL
  • Demonstrated proficiency with data science best practices, such as version control via Git or similar
  • Demonstrated proficiency with model development for decision analysis, forecasting, or other complex quantitative modeling
  • Demonstrated experience writing clear and well documented code, preferably in Python
  • Demonstrated experience working with large datasets and knowledgeable about parallelization
  • Strong understanding of statistics and experience developing supervised & unsupervised learning models

Beneficial Qualifications:

  • History mentoring and teaching others as well as desire to continue to do so
  • Demonstrated experience with data visualization tools such as Tableau, D3, Plotly, etc.
  • Enjoy working on complex multi-stage projects with a diverse team
  • Involvement or strong interest in the energy/clean tech industry
  • Familiarity with transmission and/or distribution power flow models
  • Past experience with advanced metering interval data
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