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TeamData, Analytics and Insights
Business UnitIT and Digital
Requisition No.8866
CityWaltham, US, 02451
Date posted28 May 2026
Data \& AI Director
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### About Us
The energy industry is undergoing an incredible transformation as the world decarbonizes and embraces digital. This creates significant business opportunities requiring a new way of approaching technology and data as strategic accelerators. National Grid’s New York and Massachusetts operations provide critical national infrastructure for both Gas and Electric customers.
### About The Role
The AI \& Data Director will be responsible for leading and executing the US Data and AI strategy, driving data maturity across US Gas and Electric business areas, and ensuring that data and AI are leveraged to deliver measurable business value. This role will define the vision, strategy, and roadmap for data and AI initiatives, fostering a culture of data\-driven decision\-making while ensuring governance, compliance, and innovation, and alignment with National Grid’s Enterprise Data and AI Strategy.
Reporting directly to the New York \& US Electric and Gas Chief Information \& Digital Officer, this person will be a motivational leader providing strategic direction, mentorship, and feedback, while modelling National Grid’s leadership values to foster a high\-performing, delivery\-oriented team environment. The role will partner closely with the enterprise data organization, business unit leaders, and technology teams to ensure data and AI capabilities are embedded into operations, enhancing customer experiences and operational efficiency. This is a unique opportunity to lead a data and AI organization that will help solve some of the most challenging and aggressive climate\-change goals in the world.
### Key Accountabilities
- Develop and Implement Comprehensive Data and AI Strategy
- Build upon and execute the US data and AI strategy that aligns with organizational goals and drives data maturity across all business areas
- Identify key areas of business value, set clear objectives, and establish a roadmap for initiatives
- Partner with product, architecture, and functional leadership to integrate data and AI into product roadmaps and operational processes
- Manage the data and AI budget, with ongoing focus on personnel, vendor, and capital expenditures
- Drive Innovation and AI Adoption: lead efforts to innovate and adopt AI technologies in partnership with the enterprise team. Stay updated with the latest AI trends and ensure the organization leverages AI to improve operations, enhance customer experiences, and gain a competitive edge
- Foster a data\-driven culture
- Increase data literacy across the organization, ensuring employees understand the value of data and AI, and promoting the use of data in decision\-making processes. Partner closely with the enterprise data organization to embed data capabilities into business operations
- Champion cultural change toward rapid deployment and continuous improvement in data and AI capabilities
- Ensure Data Governance and Compliance: establish and maintain strong data governance practices to ensure data quality, security, and compliance with relevant regulations. Implement data management frameworks and oversee governance initiatives
- Build and manage a strong Data team
- Oversee relationships with consultants, vendors, and contractors, supporting negotiation of statements of work and business terms with suppliers
### About You
The ideal candidate will be an experienced data and AI leader with broad and deep demonstrated subject matter expertise across data strategy, governance, analytics, and AI adoption. They will serve as a recognized thought leader and expert in all things data and AI.
- Technical and Functional Excellence: Deep expertise in data strategy, governance, analytics, AI/ML, and related technologies (e.g., cloud platforms, data warehouses, data lakes, BI tools, MLOps)
- Thought Leadership: Brings a mindset that embraces innovation and questions conventional wisdom. Expert in emerging trends in data and AI, and able to translate them into actionable strategies
- Team Leadership: Proven ability to recruit, lead, mentor, and develop high\-performing data and AI teams. Strong talent management skills including coaching, mentoring, and team motivation
- Collaboration and Influence: Excels at partnering across multiple functions to facilitate prioritization into actionable plans
- Best Practices: Demonstrated experience in establishing and maintaining data governance, quality, and compliance frameworks
- Customer Centricity: Focused on building a culture that understands and responds to customer needs through data\-driven insights
- Communication: Ability to communicate complex data and AI concepts clearly to diverse audiences, from executives to technical teams
- Innovation: Experience using AI and automation to increase operational efficiency and create competitive advantage
- Financial Acumen: Skilled in managing budgets, resource plans, and investment initiatives for data and AI functions
### Qualifications
- Strong track record in data strategy, governance, analytics, and AI leadership, with significant experience in senior management roles
- Strong leadership and people management skills, with demonstrated ability to build and lead large, high\-performing teams, and foster a data\-driven culture
- Excellent communication skills, able to develop, document, and explain data and AI strategies to diverse audiences
- Proven track record of delivering measurable business outcomes through data and AI initiatives
- Bachelor’s degree in Data Science or related discipline required; advanced degree or MBA preferred
Closing Date: Monday the 15th of June 2026\.
Location: New York, NY / Waltham, MA (or remotely based in MA, NY, CT, NJ, PA)
Hiring Manager: Erik Barthel, New York \& US Electric and Gas Chief Information \& Digital Officer
Salary Range: Between $186k \- $315k
Candidates will be assessed and provided offers against the minimum qualifications of this role and their individual experience. This range is also variable dependent on the location of the candidate.
National Grid is an equal opportunity employer that values a broad diversity of talent, knowledge, experience and expertise. We foster a culture of inclusion that drives employee engagement to deliver superior performance to the communities we serve. National Grid is proud to be an affirmative action employer. We encourage minorities, women, individuals with disabilities and protected veterans to join the National Grid team.
Salary Context
This $186K-$315K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At National Grid, 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 in Demand for This Role
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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($250K) sits 40% above the category median. Disclosed range: $186K to $315K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
National Grid AI Hiring
National Grid has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Waltham, MA, US. Compensation range: $192K - $315K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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