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Marathon Petroleum

Refines and sells gasoline, diesel and jet fuel

Decision Intelligence Director

Engineering LeadershipPosted on Nov 9
Full-timeIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Information Technology, Management Information Systems, Engineering, Business, or another computer-related degree is required.
  • At least 12 years of diversified information technology experience is required.
  • At least five years of directly managing professional staff is required.
  • Experience establishing or materially transforming an enterprise analytics, machine learning, or artificial intelligence capability from strategy through governed production adoption is required.

Responsibilities

  • Lead managers and individual contributors through guidance, coaching, and support to align assignments with organizational goals and policies; drive recruitment, development, retention, performance management, and succession planning.
  • Collaborate with key stakeholders and senior management to provide strategic guidance on technology risks, opportunities, and prioritization, ensuring cost-effective and agile solutions.
  • Oversee planning, design, implementation, and measurement of information technology systems, balancing agility with 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 with business priorities and measurable value 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 the enterprise AI capability, including its framework, platform strategy, reference architectures, reusable services, engineering patterns, evaluation standards, AgentOps practices, and operating model.
  • Establish enterprise agent and agentic AI strategy, including agent 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 enabling domain and product teams to work within common enterprise standards without making Decision Intelligence a delivery bottleneck; clarify decision rights, escalation routes, support expectations, and product accountability.
  • Lead 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 Data Architecture & Engineering, Data Products Governance, Data Operations & Platforms, Cybersecurity, Enterprise Architecture, Legal, Risk, and business leaders to embed security, privacy, responsible AI, data quality, resilience, and compliance controls.
  • Assess emerging analytics and AI technologies, including Microsoft Foundry and Azure services, Databricks, application-native agents, industrial AI platforms, and open standards, and recommend their fit within the target architecture.
  • Establish portfolio and performance measures covering 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.

Desired Qualifications

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

About the Company

Marathon Petroleum

Refines and sells gasoline, diesel and jet fuel

Marathon Petroleum refines crude oil into gasoline, diesel, jet fuel and other products for wholesale buyers and branded fuel stations across the United States. It runs what it calls the nation's largest refining system, with 13 refineries spread across the Gulf Coast, Mid-Continent and West Coast. Its fuel reaches drivers under the Marathon and ARCO brands, and it co-owns a California plant with Neste that makes renewable diesel from fats and plant oils. The company earns most of its money selling refined fuels, while its controlled partnership MPLX gathers, processes and moves natural gas, crude oil and fuel. Marathon Petroleum traces its roots to 1887 and is based in Findlay, Ohio.

Company size
10,001+
Company stage
IPO
Headquarters
Findlay, OH
Founded
1887

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