Manager, Technology Planning & Governance

Role purpose

Strategy and use cases

• Align the refinery AI agenda with business priorities and enterprise digital strategy; develop a prioritized roadmap.
• Identify opportunities across operations, maintenance, engineering, reliability, planning, energy, supply chain, inspection, and support functions.
• Assess value, feasibility, data readiness, effort, and risk; prepare concept notes, business cases, charters, KPIs, and benefit logic.

Delivery and value

• Coordinate end-to-end delivery, track milestones and interdependencies, remove barriers, and escalate issues.
• Move initiatives through assessment, design, pilot, deployment, adoption, and scale-up.
• Track value such as throughput, yield, energy, reliability, maintenance cost avoidance, HSE improvement, productivity, or margin; report status and outcomes.

Adoption, governance and vendors

• Coordinate readiness, communications, awareness, and training to embed solutions in daily work and management routines.
• Apply AI governance, risk, compliance, and control processes; coordinate with cybersecurity, data governance, architecture, legal, procurement, and HSE.
• Support scopes of work, requirements, proposal evaluation, and vendor delivery monitoring.

Qualifications and experience

• Bachelor’s degree in chemical engineering, data science, computer science, industrial engineering, or a related discipline.
• Typically 8–12 years of relevant refinery, oil and gas, petrochemical, energy, or industrial transformation experience.
• Experience in digital transformation, AI or analytics delivery, industrial operations improvement or technology implementation, stakeholder engagement, business cases, coordination, and value tracking.
• Master’s degree, transformation/project-management/AI/analytics certification, and familiarity with historians, APC, ERP, CMMS, planning, or performance reporting tools are advantageous or preferred.

Technical knowledge

• Knowledge of refinery operations, value levers, and performance drivers; understanding of AI, analytics, machine learning, and industrial digital use cases.
• Awareness of business cases, benefit tracking, change management, data governance, and OT/IT deployment considerations.

Interfaces and initiative themes