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Senior Predictive Analytics Data Scientist
PG&E Corporation
- United States
- United States
About
# 167320 Job Category:
Accounting / Finance Job Level:
Individual Contributor Business Unit:
Electric Engineering Work Type:
Hybrid Job Location:
Oakland, CA (with hybrid work availability) Department Overview The System Performance, Reliability and Resiliency Strategy team is dedicated to advancing PG&E's Electric Reliability Strategy. As an innovative group, we focus on deploying modern technologies to ensure the electric grid operates safely, resiliently, and efficiently. Our team collaborates with various internal and consulting partners to transition projects from development through testing to operational phases. Position Summary This role, reporting to the Senior Manager of Reliability Analytics, will involve creating advanced data science models and cutting-edge anomaly detection techniques to foresee potential failures, enhancing the reliability of the electric transmission and distribution grid. Key responsibilities include: Designing and implementing scripts, algorithms, and processes utilizing both structured and unstructured data from diverse sources. Developing reproducible and defensible machine learning and AI models to support strategic problem-solving and decision-making. Participating in data science communities to stay updated and contribute to the field's advancements. Educating non-technical stakeholders on the capabilities and limitations of data science solutions. Working collaboratively with data engineers, data scientists, and subject matter experts to evolve data science capabilities within the utility sector. This hybrid position will entail working remotely as well as on-site within the PG&E Service Territory based on business needs. Salary Information PG&E offers a competitive salary range based on geographical location, internal equity, and individual qualifications. The specific salary for this role is as follows: Bay Area: $126,000 - $179,300 California: $120,000 - $170,500 Key Job Responsibilities Lead the research and development of methodologies to detect potential system failures and improve grid reliability. Apply data science and machine learning techniques to create scalable and reproducible models. Serve as a technical lead for predictive reliability analytics model development. Develop Python code for data processing and model developments (e.g., ML/AI models). Document datasets and modeling processes for transparency and reproducibility. Create strategies aligned with team goals to enhance data science capabilities. Communicate technical concepts and findings to various stakeholders. Qualifications Minimum: Bachelor's Degree in Data Science, Computer Science, Engineering, or a related field. A minimum of 4 years of data science experience, or 2 years with a Master's Degree. Desired: A Ph.D. or Master's degree in relevant engineering or science fields. Experience in the electric or gas utility industry, renewable energy, or analytics consulting. A strong foundation in statistics, machine learning (ML), and artificial intelligence (AI). Practical experience in deploying data science and ML models with Python. A proven ability to solve complex, unstructured problems through data-driven methods. Experience working with large datasets from varied sources. Exceptional communication skills to convey technical concepts to non-technical audiences. Ability to mentor and guide junior data scientists in data science and ML techniques.
Languages
- English
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