Data Science Intern Job at Pacific Gas and Electric Company, Oakland, CA

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  • Pacific Gas and Electric Company
  • Oakland, CA

Job Description

Requisition ID # 163740

Job Category: Administrative / Clerical

Job Level: Individual Contributor

Business Unit: Electric Engineering

Work Type: Hybrid

Job Location: Oakland

Data Science Intern

Department Overview

The System Performance, Reliability and Resiliency Strategy team within the overall Electric Transmission and Distribution Engineering organization is responsible for planning, organizing, and managing the resources necessary to successfully execute PG&E’s Electric Reliability Strategy and initiatives. This team of forward–thinking individuals will be tasked with deploying technology and infrastructure and influencing the organization in order to achieve the company’s reliability goals. The team is responsible for implementing programs required to modernize the electric grid allowing for safe, resilient and efficient operations. The team participates in a cross functional team of internal and consulting participants being tasked with leading the transition of a project from development and testing to being operational for each phase of each project.

Position Summary

We are looking for a data science intern to join our team to help develop and deploy predictive models to identify incipient failures on the electric grid. In this role you will work as a part of the Predictive Analytics team to help improve the development of predictive and anomaly detection models. This is a role at the intersection of data science, engineering, data engineering, and data analysis. Responsibilities include, but not limited to the following workstreams:

PG&E is providing the hourly rate 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 hourly rate 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.

The hourly rate for this position ranges from $22.77 to $36.42.

Responsibilities

  • Gathering, cleaning, and analyzing the data from varied sources
  • Designing and implementing a time series data pipeline of outage, failure and monitoring data
  • Enabling frequent quantitative analysis of the effectiveness of current grid mitigations
  • Developing an interactive dashboard that includes visualization of mitigation effectiveness

The hands-on work that this position entails will provide the successful intern with knowledge about quantitative risk modeling and analytics at PG&E and knowledge of the various PG&E organizations and operations.

Qualifications

Minimum:

  • BA/BS or Master’s degree in Statistics, Computer or Data Science, Engineering or other related field
  • (students must be continuing education towards degree during and/or after the internship in a fulltime capacity)
  • Knowledge of relevant programming languages and tools such as Python, Microsoft Power BI, SQL
  • Students must be continuing their education towards degree during and/or after the internship fulltime.
  • Must have a valid driver’s license or obtain one at least 30 days prior to start date.

Desired:

  • 3.0 GPA or greater in both cumulative GPA and major GPA
  • General Knowledge of Utility Industry
  • The ability to work seamlessly in a team environment and support fellow team members
  • Strong problem solving, critical thinking, researching and analytical skills
  • Strong communication skills, both written and oral
  • Strong organizational skills such as effective work prioritization, discipline in deadline submissions, and the ability to work on multiple tasks independently
  • Experience with platforms such as Palantir Foundry and Snowflake
  • Self-starter and takes initiative

PG&E is unable to provide VISA sponsorship to students on an F-1, J-1 or other student visa for this position.

Job Tags

Hourly pay, Full time,

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