Group Head - Artificial Intelligence & Digital Transformation

Job Role: Group Head - Artificial Intelligence & Digital Transformation 

Job Summary:

• The Group Head: AI & Digital Transformation is responsible for defining and executing the Group’s enterprise-wide Artificial Intelligence and Digitisation strategy, translating emerging technologies 
  into measurable improvements in operational performance, reliability, productivity, safety, risk management and commercial value.
• The role will identify, prioritise and scale high-value AI and digital use cases across the Group’s industrial portfolio, including refining, petrochemicals, fertiliser, cement, manufacturing, mining/raw materials, logistics, transport, supply chain and corporate functions.
• The role will lead the development and deployment of AI-enabled solutions including machine learning, predictive and prescriptive analytics, computer vision, optimisation algorithms, generative AI, intelligent automation, digital twins and advanced decision-support systems.
• The Group Head will operate at the intersection of business, industrial operations, data, technology and AI, working closely with the GCRO, GCIO, business CEOs/MDs, plant leadership 
  and functional executives. The role will ensure that AI and digitisation investments move beyond experimentation to production-scale solutions with defined business outcomes, appropriate governance and measurable return on investment.
• AI & Digitisation is a centre-led Group capability, with execution embedded across business units and operating environments

Qualifications & Experience 

• Bachelor’s degree in Artificial Intelligence, Computer Science, Data Science, Machine Learning, Software Engineering, Computer Engineering, Electrical/Electronic Engineering, Robotics, 
• Mathematics, Statistics, Computational Science or related quantitative discipline.
• Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Engineering, Robotics or a related advanced technology discipline strongly preferred.
• Executive education in technology, innovation, digital transformation or business strategy would be advantageous.
• 10+ years’ relevant technology/data/AI experience, with meaningful leadership responsibility and evidence of deploying advanced technology into production environments.
• Demonstrable experience taking AI solutions from problem definition and data engineering through model development, deployment, monitoring and value realisation.
• Experience in industrial, energy, oil & gas, manufacturing, utilities, mining, logistics, infrastructure or other asset-intensive environments strongly preferred.
• Experience building or leading multidisciplinary teams comprising data scientists, ML engineers, data engineers, software engineers, product specialists and business/industrial SMEs.
• Demonstrable portfolio of successfully deployed AI/digital products with quantifiable operational or financial outcomes

Key Requirements

• Advanced understanding of machine learning, deep learning, generative AI, large language models and advanced analytics.
• Strong understanding of Python, SQL and modern AI/ML development environments; technical credibility sufficient to interrogate models, architectures and algorithms.
• Understanding of time-series modelling, forecasting, optimisation, anomaly detection and predictive modelling.
• Knowledge of computer vision, NLP, intelligent automation and agentic AI systems.
• Understanding of digital twins, IoT, edge computing, sensors and industrial data environments.
• Strong understanding of MLOps, model deployment, APIs, cloud platforms, data pipelines and model monitoring.
• Ability to apply AI to rotating equipment, process plants, production optimisation, asset integrity, reliability and industrial risk.
• Ability to translate complex industrial and business problems into mathematical, analytical and AI-solvable problems.
• Strong commercial orientation with ability to quantify ROI, productivity gains, avoided losses, downtime reduction and margin improvement.
• Strong understanding of AI governance, model risk, data privacy, cybersecurity and responsible AI.
• Exceptional strategic thinking, innovation, stakeholder influence and executive communication.
• Ability to challenge technology hype and distinguish between automation, analytics, digitisation and genuine AI applications.a