Decision Intelligence Director

Marathon Petroleum Corporation•San Antonio, TX

About The Position

The Decision Intelligence Director leads Marathon’s enterprise Decision Intelligence capability across Business Intelligence, Machine Learning, and Artificial Intelligence, including Generative AI, AI agents, and agentic systems. The role sets the strategy and operating model that connect trusted data, analytics, enterprise knowledge, and AI to improve the quality and speed of business decisions. It leads BI and Machine Learning through their respective managers and directly owns the vision, framework, platform strategy, and implementation model for enterprise AI, agents, and agentic AI. The Director partners across business, architecture, engineering, governance, cybersecurity, platform operations, and Digital Product Delivery to establish reusable enterprise capabilities, enable federated delivery, and move high-value solutions from experimentation to governed, scalable operations. The position leads managers and individual contributors, develops talent and delivery discipline, and drives measurable business value. The role defines and executes the enterprise roadmap across BI, Machine Learning, Generative AI, agents, and agentic systems; provides executive leadership for BI and Machine Learning functions; and owns the enterprise AI capability, including its framework, platform strategy, architectures, services, engineering patterns, evaluation standards, AgentOps practices, and operating model. It also establishes standards for enterprise agents, advances the Enterprise Knowledge System, creates a federated delivery model, and guides AI capabilities through exploration, pilot, production, and scale with business ownership, readiness, support, adoption, and benefits realization. The Director works with business and technology leaders and functions including Data Architecture & Engineering, Data Products Governance, Data Operations & Platforms, Cybersecurity, Enterprise Architecture, Legal, and Risk to embed security, privacy, responsible AI, data quality, resilience, and compliance. The role assesses emerging analytics and AI technologies, recommends their fit within Marathon’s target architecture, establishes portfolio and performance measures, and influences stakeholders to align priorities, investment, architecture, and delivery capacity. The position is based in San Antonio, Texas, with additional locations listed in Findlay, Ohio, and Houston, Texas. Marathon Petroleum describes its total rewards program as including access to health, vision, and dental insurance, paid time off, 401(k) matching, paid parental leave, educational reimbursement, and eligibility for a discretionary company-sponsored annual bonus program.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Management Information Systems, Engineering, Business, or another computer-related field.
At least 12 years of diversified IT experience.
At least 5 years directly managing professional staff.
Demonstrated experience establishing or materially transforming an enterprise analytics, machine learning, or AI capability from strategy through governed production adoption.

Nice To Haves

Experience leading multiple analytics or AI disciplines through managers and senior technical leaders.
Experience defining enterprise AI architecture, platform capabilities, operating models, governance, and lifecycle controls for agents or Generative AI.
Experience in a complex, regulated, asset-intensive, energy, manufacturing, or industrial enterprise.

Responsibilities

Lead managers and individual contributors through guidance, coaching, and support, ensuring assignments align with organizational goals and policies.
Drive recruitment, development, retention, performance management, and succession planning.
Collaborate with stakeholders and senior management to provide strategic guidance on technology risks, opportunities, and prioritization, supporting cost-effective and agile solutions.
Oversee the planning, design, implementation, and measurement of IT systems while balancing agility, stability, security, and efficiency.
Define and execute the enterprise Decision Intelligence strategy and roadmap across Business Intelligence, Machine Learning, Generative AI, AI agents, and agentic systems, aligned to business priorities and measurable outcomes.
Provide executive leadership for Business Intelligence, including enterprise reporting, dashboards, self-service analytics, trusted semantic models, reusable KPI definitions, adoption, and governance alignment.
Provide executive leadership for Machine Learning, including advanced analytics, predictive and optimization models, computer vision, model lifecycle management, MLOps integration, production monitoring, and value realization.
Own the vision and implementation of enterprise AI capabilities, including the AI framework, platform strategy, reference architectures, reusable services, engineering patterns, evaluation standards, AgentOps practices, and operating model.
Establish enterprise agent and agentic AI strategy covering discovery and registration, identity, approved tools and interfaces, orchestration, human approval, evaluation, observability, lifecycle management, and safe escalation or stopping controls.
Advance the Enterprise Knowledge System by connecting governed data products, semantic models, metadata, business context, ontology and knowledge graphs, documents, skills, tools, and prior decisions into reusable context for analytics and AI.
Define placement and integration patterns for application-native agents, data- and AI-intensive agents, and cross-system enterprise agents while preserving authoritative systems, business ownership, and enterprise controls.
Create a federated delivery model that enables domain and product teams to build within common enterprise standards without making Decision Intelligence a delivery bottleneck; clarify decision rights, escalation routes, support expectations, and product accountability.
Lead the progression of AI capabilities from exploration to pilot, production, and scale, requiring business ownership, success measures, production readiness, operating support, adoption plans, and benefits realization.
Partner with technical, governance, security, legal, risk, and business teams to embed security, privacy, responsible AI, data quality, resilience, and compliance controls into solutions.
Assess emerging analytics and AI technologies, including Microsoft Foundry and Azure services, Databricks, application-native agents, industrial AI platforms, and relevant open standards; recommend their fit within Marathon’s target architecture.
Establish portfolio and performance measures for business outcomes, adoption, quality, reliability, cost, model and agent performance, risk, reuse, and operational health.
Influence senior business and technology stakeholders to prioritize high-value opportunities, resolve cross-functional ownership decisions, and align investment, architecture, and delivery capacity.