AI Delivery Lead

bp•Denver, CO

13d•$170,000 - $200,000•Hybrid

About The Position

bpx energy, a major oil and gas producer in the United States, leverages its expertise in unconventional gas, including shale, to deliver hydrocarbon production and technical knowledge worldwide. With operations in Texas and Louisiana, our US onshore business has become both an outstanding oil and gas producer and a leader in reducing methane emissions. As part of BP, a global industry leader, we champion a high-energy, high-intensity environment built on accountability, collegiality, and empowerment. The AI Delivery Lead is responsible for improving the efficiency, rigor, and business impact of AI delivery across the organization. This role serves as the bridge between business objectives, AI solution development, adoption, and value realization. The successful candidate will help AI teams move beyond experimentation and technical outputs toward measurable business outcomes. They will establish delivery standards, mentor practitioners, challenge weak assumptions, improve decision-making, and ensure AI solutions are grounded in business needs, credible data, appropriate evaluation methods, and measurable results. This role reports directly to the Head of AI Innovation & Strategy and serves as a leader responsible for raising the overall maturity of AI delivery practices. This is not a research-focused role. Success will be measured by adoption, business impact, rather than model development activity, experimentation volume, or technical complexity.

Requirements

10+ years of experience delivering technology, analytics, AI, data science, or digital products in sophisticated enterprise environments.
Proven experience delivering AI, analytics, or decision-support solutions that achieved measurable adoption and business impact.
Strong understanding of experimental design, base-lining, model evaluation, data quality, and performance measurement.
Proven track record to decompose ambiguous business problems into executable delivery plans.
Experience coaching technical teams and improving delivery discipline.
Strong business acumen and ability to connect technical work to operational and financial outcomes.
Excellent communication and stakeholder leadership skills.
Experience operating in environments with significant ambiguity and evolving requirements.

Nice To Haves

Experience in energy, manufacturing, logistics, industrial operations, automotive, aviation, or other asset-intensive industries.
Experience leading AI product delivery, sophisticated analytics programs, operational optimization initiatives, or digital transformation efforts.
Consulting, product delivery, or transformation leadership experience.
Experience working with cross-functional teams including business leaders, engineers, architects, product owners, operations teams, and executives.
Familiarity with MLOps, model governance, responsible AI practices, and enterprise AI operating models.

Responsibilities

Lead AI initiatives from problem definition through adoption and value realization.
Establish and enforce AI delivery standards.
Develop repeatable delivery practices for problem framing, experimentation, evaluation, deployment, adoption, and value measurement.
Ensure AI initiatives are aligned to business priorities and operational outcomes.
Help teams distinguish between research activity, technical progress, prototype completion, and realized business value.
Partner with business stakeholders to understand operational challenges, decision points, workflows, and desired outcomes.
Decompose sophisticated business problems into deliverable work streams, testable hypotheses, and measurable objectives.
Identify when AI is appropriate and when simpler solutions may be more effective.
Ensure all AI initiatives begin with a clearly defined problem statement, baseline, target user, and success criteria.
Challenge poorly defined use cases and redirect efforts toward opportunities with measurable business value.
Serve as an independent quality gate for AI initiatives.
Review and challenge assumptions, solution approaches, datasets, evaluation methods, value estimates, and deployment readiness.
Require appropriate baselines before model development or optimization efforts begin.
Ensure model improvement claims are supported by objective evidence and appropriate evaluation methodologies.
Verify that data sources are fit for purpose and representative of operational reality.
Possess the authority to pause, redirect, or require rework for initiatives that do not meet delivery standards.
Establish standards for base-lining, experimentation, evaluation, and benefits tracking.
Ensure teams differentiate between theoretical value, potential value, embraced value, and realized value.
Develop practical approaches for measuring business impact and operational adoption.
Validate value claims prior to executive reporting.
Supervise the extent to which solutions influence decisions, improve workflows, or build measurable business outcomes.
Ensure business cases remain grounded in realistic adoption assumptions and observable outcomes.
Mentor AI engineers, data scientists, analysts, and delivery team members on practical AI delivery fundamentals.
Provide mentorship on problem decomposition, stakeholder engagement, data quality assessment, baseline creation, experimentation design, and value measurement.
Review team deliverables and provide direct feedback on quality, rigor, communication, and business relevance.
Raise expectations for analytical rigor, intellectual honesty, and business accountability.
Help establish a culture that values measurable outcomes over technical activity alone.
Translate technical concepts into clear business language for executives and operational leaders.
Help teams communicate the “why” behind AI initiatives, not just the technical solution.
Facilitate alignment between business stakeholders, technical teams, and leadership.
Develop concise, credible executive communications regarding progress, risks, adoption, and business outcomes.
Build trust by ensuring AI efforts remain transparent, measurable, and outcome-oriented.