Interested in this Data Scientist role at Pacific Gas and Electric?
Apply Now →Skills & Technologies
About This Role
Requisition ID # 174571
Job Category: Accounting / Finance
Job Level: Individual Contributor
Business Unit: Strategy & Growth
Work Type: Hybrid
Job Location: Oakland
Department Overview
Over 6,500 coworkers in Electric T&D ensure the delivery of clean, safe, reliable and affordable energy to nearly 16 million people in Northern and Central California. The Electric T&D 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 T&D's 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.
This position is headquartered in Oakland, CA. This position is “hybrid”, with the expectation of being in the office 1x/week (subject to change due to business needs. This position is within the Transmission Tools and Modeling (TTM) team. TTM develops the analytical tools, data solutions, and power system models that enable informed transmission planning and operational decision-making. The team combines expertise in power system analysis, software development, data engineering, and process automation to build and maintain study cases, planning models, databases, and forecasting methodologies used across the organization. Working closely with planners, engineers, operations, and business partners, the team delivers innovative solutions that improve study efficiency, data quality, and modeling accuracy while supporting the reliable and cost-effective planning of the electric grid. The group is focused on modernizing workflows, leveraging advanced analytics, and creating scalable tools that help transform complex grid data into actionable insights.
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:
Bay Area Minimum: $140,000
Bay Area Maximum: $238,000
&/OR
California Minimum: $133,000
California Maximum: $226,000
Position Summary
Designs, develops, and executes scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Participates in internal and external communities of practice in data science/artificial intelligence/machine learning to advance knowledge in the field. Educates the non-technical community on advantages, risks, and maturity levels of data science solutions.
Responsibilities:
Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
Creates advanced 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 that involve multiple facets and iterations in algorithm development.
Wrangles and prepares data as input of machine learning model development and feature engineering
Writes and documents reusable python functions and modular python code for data science.
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.
Presents findings and makes recommendations to senior management.
Act as peer reviewer of complex models
Qualifications
Minimum Qualifications:
Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
6 years in data science (or no experience, if possess Doctoral Degree or higher, as described above).
Desired Qualifications:
Doctoral Degree or higher 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
Active participation in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through volunteering in professional organizations for the advancement of the field, presentations in conferences or publications to disseminate data science knowledge and topics, or similar activities.
Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc) along with best practices to implement them
Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities
Competency with commonly used data science and/or operations research programming languages, packages, and tools for building data science/machine learning models and algorithms
Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders
Mastery of the mathematical and statistical fields that underpin data science
Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals
Previous experience working with utility data
Previous experience with load forecasting, working with utility customer data, consumption patterns, and outage data
Working knowledge power flow concepts and tools such as PLSF, PSS®E, TARA, PROMOD, PLEXOS
Previous experience with cloud-based infrastructure services across compute, storage, network, and operating system layers along with an understanding of cloud architecture
Salary Context
This $133K-$238K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Pacific Gas and Electric, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $133K to $238K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Pacific Gas and Electric AI Hiring
Pacific Gas and Electric has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Oakland, CA, US. Compensation range: $194K - $265K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.