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About This Role
Join the Clean Energy Revolution
Become a Senior Data Science Manager at Southern California Edison (SCE) and build a better tomorrow. In this job, you’ll be leading a highly talented and dynamic data science team in developing SCE’s asset risk models and advancing SCE’s data\-driven, science\-based strategies.
As a Senior Data Science Manager, your work will help power our planet, reduce carbon emissions and create cleaner air for everyone. Are you ready to take on the challenge to help us build the future?
Responsibilities
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- Steers cross\-functional business cases using advanced data modeling and analysis techniques to discover insights that will guide strategic decisions and uncover optimization opportunities.
- Directs large\-scale, data science projects that leverage data transformation and machine learning models.
- Oversees the design and delivery of reports and insights that analyze business functions and key operations and performance metrics.
- Manages key initiatives for SCE and ensures data science and predictive analytics alignment with business strategies and outcomes.
- Delivers advanced analytics and AI use cases by engaging across the organization with all levels of the organization incorporates.
- Addresses business problems and leads the design, evolution, and validation of models and algorithms to ensure best performance and solve business problems; collaborates with engineers to deploy models and track model performance.
- Drives the development of predictive modelling of key assets to inform risk analysis and mitigation strategies in support of business initiatives and for regulatory filings.
- Champions the adoption of data\-driven decision\-making processes throughout the organization by effectively communicating results and insights to stakeholders.
- Fulfills advanced analytical initiatives to support key strategic priorities and focus areas.
- Monitors the quality of data architecture models determining whether or not they are current, fit for purpose and reflective of the market, collaborates with the use\-case leads to ensure previously developed data models are reused or adapted.
- A material job duty of all positions within the Company is ensuring the protection of all its physical, financial and cybersecurity assets, and properly accessing and managing private customer data, proprietary information, confidential medical records, and other types of highly sensitive information and data with the highest standards of conduct and integrity.
Minimum Qualifications
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- Five or more years of experience supervising a team of direct reports and/or project management. Experience leading a technical/analytical team.
Preferred Qualifications
- Master’s or Ph.D. degree in computer science, statistics, mathematics, engineering, or related STEM discipline.
- Five or more years hands\-on experience working in data science or analytics roles, including experience with machine learning algorithms, statistical modeling, and big data platforms.
- Hands\-on experiences with languages like Python, R, and SQL, as well as knowledge of cloud computing environments (e.g., AWS, Azure, Google Cloud), and familiarity with data visualization tools like Tableau or Power BI.
- Experience leading regulatory process activities and filings by developing analyses, workpapers, testimony, data request responses, and reports for agency proceedings, while applying risk‑management tools and frameworks including CPUC’s Risk‑Based Decision‑Making and Wildfire Mitigation Plans and perform benefit‑cost analyses that inform investment decisions.
- Experience evaluating and deploying artificial intelligence solutions into business processes.
Additional Information
- This position’s work mode is hybrid. The employee will report to an SCE facility for a set number of days with the option to work remotely on the remaining days. Unless otherwise noted, employees are required to work and reside in the state of California. Further details of this work mode will be discussed at the interview stage. The work mode can be changed based on business needs.
- Visit our Candidate Resource page to get meaningful information related to benefits, perks, resources, testing information, hiring process, and more!
- Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
- The primary work location for this position is Pomona, CA. However, the successful candidate may also be asked to work for an extended amount of time at (alternate work location).
- Relocation may apply to this position.
About Southern California Edison
The people at SCE don't just keep the lights on. Our mission is so much bigger. We’re fueling the kind of innovation that’s changing an entire industry, and quite possibly the planet. Join us and create a future with cleaner energy, while providing our customers with the safety and reliability they demand. At SCE, you’ll have a chance to grow personally and professionally, making a real impact in Southern California and around the world.
Southern California Edison is a proud Equal Opportunity Employer, including disability and protected veteran status.
We are committed to ensuring that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodations at (833\) 343\-0727\.
Salary Context
This $178K-$267K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Southern California Edison, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 33% above the category median. Disclosed range: $178K to $267K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Southern California Edison AI Hiring
Southern California Edison has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Pomona, CA, US, Big Creek, CA, US. Compensation range: $267K - $267K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
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