Senior Data Scientist

Requisition ID # 175422 

Job Category: Engineering / Science 

Job Level: Individual Contributor

Business Unit: Energy Delivery

Work Type: Hybrid

Job Location: Danville; San Ramon

Department Overview  

Electric Transmission & Distribution ensures the delivery of clean, safe, reliable and affordable energy to nearly 16 million people in Northern and Central California. The Electric Transmission & Distribution Team is responsible for ensuring the electric grid's reliability and resiliency through the ongoing maintenance and operations of PG&E's 100,000 miles of distribution lines and over 18,000 miles of Transmission lines. Coworkers in Electric Transmission & Distribution actively engage in the Lean Methodology through the use of visual management in operating reviews to cross-functionally identify and solve problems, and create standard work. By working together as One Electric. One Team. We are delivering for our hometowns. Electric Transmission & Distribution is comprised of approximately 6,500 coworkers. 

Position Summary  

The Applied Technology Services (ATS) staff provides advanced analytical, modeling, testing, and risk assessment services across multiple disciplines to support company organizations in identifying, quantifying, and addressing complex operational and asset-related risks. The team develops and applies statistical, probabilistic, and machine learning-based approaches to evaluate system performance, asset condition, degradation trends, and probability of failure, enabling data-driven decision making for reliability, resilience, and lifecycle management.  

Staff integrate and analyze diverse data sources to develop predictive models, risk metrics, and analytical tools that support asset strategy, maintenance planning, investment prioritization, and operational decision making. Services include asset health assessments, reliability and risk evaluations, failure investigations, scenario analyses, and development of quantitative methodologies for complex infrastructure systems, including transmission and distribution assets, distributed energy resources, and generation facilities.  

The team provides technical consultation and analytical insights to internal clients, helping organizations proactively manage risk and improve asset performance through the application of advanced modeling techniques, data analytics, and industry best practices. Staff collaborate with business and technical stakeholders to develop analytical solutions, support project proposals, and communicate complex model results and risk insights to decision makers. Incumbents maintain knowledge of and adhere to all company and regulatory safety, quality, and governance requirements.  

This position is hybrid, working from your remote office and your assigned work location based on business need.  

The selected candidate must live within PG&E’s service territory. 

PG&E is providing the salary 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 salary 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.  Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors.​ 

A reasonable salary range is: 

Minimum Base Salary (Bay Area)            $126,000.00

Mid Base Salary (Bay Area)        $163,000.00

Maximum Base Salary (Bay Area)           $200,000.00

Job Responsibilities   

• Researches and applies knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions. 

• Creates data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets. 

• Extracts, transforms, and loads data from dissimilar sources from across PG&E for their machine learning feature engineering. 

• Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models, 

• Co-develops mathematical models and AI simulations that represent complex business problems. 

• Writes and documents python code for data science (feature engineering and machine learning modeling) independently. 

• Develops and presents summary presentations to business. 

• Develops statistical, probabilistic, and machine learning models for asset condition assessment, degradation analysis, probability of failure estimation, and risk quantification to support risk-informed decision making. 

• Develops and maintains probability of failure models, risk metrics, and analytical tools using asset condition, inspection, operational, environmental, and historical performance data to support asset strategy, reliability, maintenance, and lifecycle decisions. 

• Performs model evaluation, sensitivity and scenario analyses, and quantification of uncertainty, and communicates model results, assumptions, failure drivers, and risk insights to engineering, asset management, operational, and business stakeholders. 

Qualifications

Minimum

• Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.


• 4 years in data science (or 2 years, if possess Master’s Degree, as described above).

Desired:

• Master’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.


• Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience


• Demonstrated knowledge of and abilities with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them


• Competency in software engineering, statistics, and machine learning techniques as they apply to data science deployment


• Competency in commonly used data science and/or operations research programming languages, packages, and tools


• Hands-on and theoretical experience of data science/machine learning models and algorithms


• Ability to synthesize complex information into clear insights and translate those insights into decisions and actions. Demonstrated ability to explain in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.


• Competency in the mathematical and statistical fields that underpin data science


• Mastery in systems thinking and structuring complex problems


• Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies