AI Strategy - Oil & Gas - Manager - Consulting

AI Strategy - Oil & Gas Sector Manager - Consulting

Join EY to shape your future with confidence in the AI & Data practice focused on the Oil & Gas sector. This role offers the opportunity to lead high-impact client engagements, deliver AI strategy, and drive measurable outcomes in upstream, midstream, and downstream operations.

As part of EY’s growing AI & Data practice, we are seeking a highly experienced Senior Manager to lead enterprise AI strategy and quantitative modeling efforts for our clients in the Oil & Gas sector. This individual will bring deep industry expertise and a proven track record of designing and operationalizing responsible, scalable, and value-aligned AI solutions. You’ll lead high-impact client engagements focused on Generative AI, Agentic AI, MLOps, and AI governance frameworks — driving measurable outcomes in upstream, midstream, and downstream operations.

• Manage engagement delivery, ensuring quality and risk management throughout the project lifecycle.
• Manage client relationships, focusing on revenue generation and the identification of new opportunities.
• Manage resource plans and budgets for engagements, ensuring alignment with performance objectives.
• Define and implement enterprise-wide AI and quantitative modeling strategy tailored to oil & gas value chains (e.g., asset optimization, drilling, trading, predictive maintenance).
• Establish AI governance frameworks that ensure responsible AI adoption, ethical use of data, model risk management, and alignment with evolving regulations.
• Design and operationalize Agentic AI solutions to automate reasoning, planning, and decision-making tasks in complex environments.
• Drive the prioritization of AI use cases based on business value, feasibility, and risk, ensuring ROI on AI initiatives.
• Lead multidisciplinary teams of data scientists, engineers, and consultants to deliver end-to-end AI platforms and solutions.
• Partner with senior business and IT leaders to identify strategic opportunities and shape AI-enabled business transformation.
• Implement and scale Model Ops and MLOps practices, ensuring transparency, reproducibility, and monitoring of models in production.
• Lead AI solution architecture, including hybrid deployments on cloud (e.g., Microsoft Azure, Amazon AWS).
• Serve as a thought leader in emerging AI technologies, including Generative AI, foundation models, RAG and Agentic AI.
• Drive internal capability building and innovation in Responsible AI, agentic workflows, and energy sector-specific solutions.

• Strong analytical and decision-making skills to develop solutions to complex problems.
• Proven experience in managing client relationships and leading teams.
• Ability to communicate effectively and influence stakeholders at all levels.

• Bachelor’s degree required;
  Master’s degree preferred with focus in Computer Science, Applied Math, or related field with prior consulting experience required.
• 5+ years of experience in technology consulting, digital transformation, or AI-driven business solutions.
• 3+ years of leading AI/ML projects, including team management and executive stakeholder engagement.
• Typically, no less than 5 years of relevant experience.
• Strong expertise in AI Platforms and Tools.
• Proficiency in Data Architecture Design and Modelling.
• Experience in Digital Transformation and IT Effectiveness Assessment.
• Knowledge of Emerging Technologies and Technology Strategy, Vision, and Roadmap.
• Ability to build and manage relationships effectively.
• Strong exposure to oil & gas industry operations, value levers, and use case landscape.
• Proven success in developing AI strategy and governance models, including frameworks for Responsible AI, risk, and compliance.
• Hands‑on experience with Generative AI frameworks (e.g., OpenAI, Hugging Face, Lang Chain, RAG).
• Experience architecting and scaling MLOps platforms and data science workflows in cloud‑native environments.
• Proficiency in Python and tools like Pandas, PyTorch, Scikit‑learn, Spark, SQL.
• Experience with CI/CD, containerization (e.g., Docker, Kubernetes), and MLFlow or similar tools.
• Strong client‑facing skills with the…