Lead Value Engineer - Energy

Responsibilities

Identify & Frame Value

• Translate Strategy to Operations: Analyze the client’s strategic priorities and map end-to-end energy operations (e.g., exploration and production, refining, or grid distribution) to AI-driven improvement use cases.
• Target High-Impact Areas: Architect solutions that connect data across the value chain, focusing on critical energy operations like predictive maintenance workflows, supply chain optimization, outage reporting automation, and asset performance dashboards.
• Build the Business Case: Construct robust, data-backed business cases by transforming operational and cost data into scored, visually ranked use cases with investment estimates and projected ROI.
• Win Expansion: Build demos, develop value narratives and ROI frameworks, and present directly to C-level stakeholders across major energy firms to generate massive opportunity pipelines.
• Assess Value: Undertake customer-facing value discovery and deliver proof-of-value projects for global Tier-1 energy and utility corporations.

Realize Value

• Drive Engagement Success: Lead the value workstream by designing and deploying AI-powered feedback agents capable of ingesting 10,000+ operational data points to surface recurring themes and friction points in the field or at the plant.
• Orchestrate Improvement: Guide customers on how to use AI and automation to streamline complex operational workflows, from field service management to grid modernization.
• Solve Systemic Challenges: Spearhead process redesigns by mapping current-state bottlenecks, embedding stringent HSE (Health, Safety, and Environmental) and regulatory compliance requirements, and automating the end-to-end workflow.
• Facilitate Workshops: Facilitate workshops with C-suite and plant/department leads, scoring capabilities across multiple dimensions and benchmarking against industry peers.
• Initiate Change: Conduct customer discovery, define success metrics and KPIs, and run successful pilots to onboard thousands of users across corporate and field locations.
• Present Results: Present executive-ready business cases that convert engagements into new revenue.

Scale Value

• Codify Best Practices: Build client-facing tools that enable enterprise-wide teams to assess automation opportunities across disparate regional assets.
• Mentor & Enable: Act as a senior resource for the team, leveraging founder-level experience to design scalable operational processes and build performance dashboards to track financial and operational KPIs (e.g., asset uptime, yield optimization).
• Product Feedback: Serve as a bridge between the customer and Product teams, reducing quarterly analysis times from days to hours and saving significant operational costs.
• Manage the Journey: Lead end-to-end value discovery, business case development, and MVP delivery, securing C-suite approval for targeted operational efficiency improvements.
• Build Roadmaps: Deliver multi-year, multi-billion dollar strategic transformation roadmaps to embed sustainable, capital-efficient optimization into the client's organization.
• Drive Innovation: Provide feedback to our product development teams based on hands-on AI agent development, LLM/prompt engineering, and API integration experience.

Qualifications

• Deep Industry Experience: 10 years in the Energy sector (Oil & Gas, Power generation, or Utilities). Experience in Management Consulting (Strategy & Operations Transformation) is required.
• Domain Expertise: Granular knowledge of asset lifecycle management, upstream/downstream operations, grid modernization, and the broader energy value chain.
• Growth Mindset: Proven track record of managing the full customer lifecycle, with demonstrated ability to land new accounts, expand footprint through strategic upselling, and drive long-term value.
• Value Realization Skills: Track record of identifying operational inefficiencies and driving them to resolution. Understand how to impact the P&L and manage Capex/Opex through process improvement, AI integration, and scalable technology deployment.
• Technical Fluency: Good knowledge of Business Software/SaaS applications and Enterprise Asset Management systems (SAP S/4HANA, IBM Maximo, AVEVA, GE Digital APM), and experience with data visualization, SQL, Python, or deploying LLMs/AI models is a strong plus.
• Stakeholder Management: Experience presenting to and working with senior stakeholders. Navigate complex internal structures and communicate SaaS value and complex AI concepts to non-technical audiences.
• Builder Mentality: Comfortable working with ambiguity, adapting to a rapidly evolving AI landscape.
• Degree: B.S. or M.S. in Engineering (Petroleum, Mechanical, Electrical), Energy Management, Business Administration, Computer Science, or Industrial & Operations Engineering. Certifications in Machine Learning, Artificial Intelligence, or Data Analytics highly preferred.