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Data Scientist

PG&E Corporation -
Oakland, CA

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Job Details

Qualifications

AI models
Doctoral degree in statistics
Master's degree in physics
Economics
6 years
Statistics
Doctoral degree
Computer Science
Predictive modeling analysis
Bachelor's degree in electrical engineering
Bachelor's degree in statistics
Bachelor's degree in Physics
Big data
Coaching
Mathematics
Reliability analysis
Renewable energy
No experience needed
Bachelor's degree in economics
Doctoral degree in economics
Experience working in the energy & utilities sector
Doctoral degree in Computer Science
Team development
Research project technical leadership
Machine intelligence
Outlier detection
Bachelor's degree in engineering
Statistics research
Ph.D. in statistics
Model deployment

Full Job Description

Job Location: Oakland

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 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
Within the System Performance, Reliability and Resiliency Strategy team, this position reports to the Senior Manager of Reliability Analytics and is responsible for developing advanced data science models and industry-leading anomaly detection techniques to identify potential failures and enhance the reliability of the electric transmission and distribution grid.

In this role, the successful candidate will be uniquely positioned at the forefront of utility industry analytics. Working as part of cross-functional teams, including data engineers, data scientists, technologists, and subject matter experts – this individual will lead the development of data science capabilities that could lead to paradigm changes in how the utility operates.

This position is hybrid, working from your remote office and your assigned work location based on business need. The assigned work location will be within the PG&E Service Territory.

PG&E is providing the salary range that can reasonably be expected for this position at the time of the job posting. This salary range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, internal equity, specific skills, education, licenses or certifications, experience, market value, and geographic location. The decision will be made on a case-by-case basis related to these factors. This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.

Bay Area – $140,000 - $207,900

And/or

California - $133,000 - $198,000

Job Responsibilities

Lead research and development of state-of-the-art methodologies to detect potential system failures and improve the reliability of the electric transmission and distribution grid.
Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible models,
Serves as the technical lead for the development of predictive/reliability analytics models.
Develops python codes for data processing and data science model developments (e.g., ML/AI models, advanced statistical models)
Documents datasets, modeling processes, and result to ensure transparency, reproducibility, and defensibility.
Contribute to the development of data science strategies aligned with system performance, reliability, and resiliency team goals.
Communicate technical concepts and model results to internal/external stakeholders.
Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
Works with sponsor departments and company subject matter experts to understand application and potential of data science solutions that create value.
Act as peer reviewer of complex models

Qualifications

Minimum:
Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
Experience in Data Science, 6 years or no experience, if possess Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.

Desired:
Doctorate degree with 5+ years or Master’s degree with 8+ years in Electrical Engineering, Mechanical Engineering, Operations Research, Transportation Engineering, Physics, Applied Sciences, Statistics, or job-related discipline or equivalent experience
Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience
Active participation in professional communities related to utility reliability, such as IEEE Power and Energy Society (PES), is a plus.
Strong foundation in statistics, machine learning (ML), and artificial intelligence (AI).
Hands-on and theoretical experience in developing and deploying data science and ML models using Python.
Proven ability to formulate and solve unstructured, complex problems using data-driven approaches.
Proficiency in working with large datasets, including structured and unstructured data from diverse sources.
Excellent communication skills, with the ability to explain technical concepts to non-technical audiences.
Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies

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