Decision Intelligence Director

Marathon Petroleum Corporation•Findlay, OH

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 connecting trusted data, analytics, enterprise knowledge, and AI to improve the quality and speed of business decisions. It leads the BI and Machine Learning functions through their respective managers and directly owns the vision, framework, platform strategy, and implementation model for enterprise AI and agentic capabilities. 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 into governed, scalable operations. The role leads managers and individual contributors, develops talent and delivery discipline, and ensures measurable business value. It also establishes enterprise approaches to AI architecture, knowledge systems, agent lifecycle and controls, production readiness, portfolio measurement, and responsible adoption.

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.
Strategic outlook and ability to develop long-term plans aligned with organizational and business strategy.
Business acumen and ability to connect decisions and recommendations to business strategy.
Results-driven leadership, operational and process excellence, collaboration, timely decision-making, and accountability for results.
Clear and credible verbal and written communication, active listening, and constructive dialogue.
Continuous improvement and value-creation orientation.
Ability to energize and engage an organization.
Commitment to ongoing learning and self-development.
Ability to select, assess, coach, and develop talent and build high-performing teams.
Adaptability to changes in responsibilities, people, processes, structure, or culture.
Ability to apply data and analytics to informed decision-making and operational improvement.
Digital awareness, including learning about and evaluating emerging digital tools, technologies, and trends.
Ability to influence others and build support, trust, and confidence.

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

Leads managers and individual contributors through guidance, coaching, and support, ensuring assignments align with organizational goals and policies; drives recruitment, development, retention, performance management, and succession planning.
Collaborates with stakeholders and senior management to provide strategic guidance on technology risks, opportunities, and prioritization, ensuring cost-effective and agile solutions.
Oversees the planning, design, implementation, and measurement of IT systems while balancing agility, stability, security, and efficiency.
Defines and executes 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.
Provides executive leadership for Business Intelligence, including enterprise reporting, dashboards, self-service analytics, trusted semantic models, reusable KPI definitions, adoption, and governance alignment.
Provides executive leadership for Machine Learning, including advanced analytics, predictive and optimization models, computer vision, model lifecycle management, MLOps integration, production monitoring, and value realization.
Owns 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.
Establishes strategy for enterprise agents and agentic AI, including discovery and registration, identity, approved tools and interfaces, orchestration, human approval, evaluation, observability, lifecycle management, and safe escalation or stopping controls.
Advances 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.
Defines 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.
Creates a federated delivery model that enables domain and product teams to build within common enterprise standards without making Decision Intelligence a delivery bottleneck; clarifies decision rights, escalation routes, support expectations, and product accountability.
Leads AI capabilities through exploration, pilot, production, and scale, requiring business ownership, success measures, production readiness, operating support, adoption plans, and benefits realization.
Partners 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.
Assesses emerging analytics and AI technologies, including Microsoft Foundry and Azure services, Databricks, application-native agents, industrial AI platforms, and relevant open standards, and recommends how they fit the target architecture.
Establishes portfolio and performance measures for business outcomes, adoption, quality, reliability, cost, model and agent performance, risk, reuse, and operational health.
Influences senior business and technology stakeholders to prioritize high-value opportunities, resolve cross-functional ownership decisions, and align investment, architecture, and delivery capacity.