Value Chain Model & Optimization Advisor/Engineer

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Position Summary

Do you have a strong technical background? Do you like building tools and data pipelines that power complex business decisions? Marathon Petroleum Company LP (MPC) has an opening within the Value Chain Optimization organization on the Value Chain Insights team. Our mission is to power confident decisions across the value chain — building the trusted tools and data foundations that drive enterprise optimization and delivering the cutting-edge analytics and insights that increase trust and confidence in our reporting and reducing barriers to timely, actionable insight.

This role sits in the Model Interpretation vertical and is an individual contributor responsible for designing, building, and supporting enterprise optimization model case input tools that support Marathon's routine planning and optimization models across the value chain. Case input tools are applications that assemble, validate, and format the data required to run Marathon's Aspen PIMS and Aspen Petroleum Supply Chain Planner (PSCP) models for the full planning cycle, post-audits, and enterprise-wide optimization. This role requires expert-level Excel VBA development, strong SQL and data-pipeline skills, and a disciplined approach to building standardized, robust, low-maintenance solutions. Success depends on the ability to turn inconsistent or manual case-build processes into reliable, repeatable tooling; to partner closely with regional planning teams; and to bring engineering rigor, creative problem-solving, and attention to detail to every deliverable. This position has exposure and impact on other workstreams within the Model Interpretation team that include AI-assisted analytics and business intelligence platforms.

This position belongs to a family of jobs with increasing responsibility, competency, and skill level. Actual position title and pay grade will be based on the selected candidate’s experience and qualifications.

Key Responsibilities

• •Demonstrates advanced expertise in enterprise optimization models and translates complex model structures, assumptions, and outputs into practical business guidance and scalable solutions.
• •Designs and delivers model-related capabilities, including analysis tools, standardized model components, documentation, and AI-assisted solutions that improve consistency, understanding, and adoption.
• •Independently analyzes model performance, sensitivities, assumptions, and exceptions across multiple assets, planning horizons, or functional areas, resolving complex and ambiguous issues.
• •Partners with modeling, planning, scheduling, and platform teams to validate assumptions, constraints, configurations, and outputs against operational realities while supporting model deployment, change events, and governance activities.
• •Leads development, harmonization, and sustainment of model standards, methodologies, definitions, and supporting capabilities to improve consistency, comparability, and scalability across the enterprise.
• •Establishes and maintains documentation, training materials, and support processes that enable consistent adoption and sustained capability utilization.
• •Serves as a trusted advisor between modeling teams, business users, and governance stakeholders, creating feedback mechanisms that continuously improve model quality, usability, and business value.
• •Leads projects and improvement initiatives with moderate complexity and resource requirements while mentoring less experienced team members.

Minimum Qualifications

• •Bachelor's degree in Engineering required. A Bachelor's degree in another field may be considered with five (5) years of planning, optimization, simulation, mathematical modeling, analytics, decision-support systems, or closely related experience required.
• •Four (4) years of experience working with planning, optimization, scheduling, analytics, operations research, or decision-support models and tools.

Preferred Qualifications

• •Expert-level skills in building complex, maintainable, automated tools for production business processes, demonstrated through Microsoft Excel and VBA or equivalent technologies (e.g., Python, SQL/database automation, or other scripting and application-development platforms).
• •Strong SQL skills, including writing and tuning queries against relational databases (e.g., SQL Server) to extract and shape large, multi-table datasets.
• •Hands-on data pipeline and ETL experience—sourcing, transforming, validating, and loading data from multiple enterprise systems into standardized formats.
• •Demonstrated ability to design standardized, robust, low-maintenance tooling and templates that are scalable and easy for non-technical users to operate.
• •Familiarity with linear programming, DPO, or refinery optimization models (e.g., Aspen PIMS, PIMS-AO, XPIMS, Aspen Petroleum Supply Chain Planner) and the data structures they require.
• •Exposure to refinery or value-chain planning processes such as near-term, short-term, mid-term, back casting, or cross-regional optimization.
• •Experience manipulating and analyzing complex, high-volume, high-dimensional data from varying sources.

Working knowledge of Power Query, Power BI, Python, or other data-transformation and automation tools.

Proven ability to think critically and solve problems given incomplete information and/or ambiguous requirements, delivering results at varying levels of precision.

Strong documentation habits and a collaborative approach to supporting business users.

Skills

• •AI Fundamentals: Understanding of core AI concepts and methods, ability to apply AI to job-relevant use cases and capacity to contribute to organizational AI reimagination.
• •Analytical Thinking: Strong problem-solving skills to identify architectural challenges, analyze requirements, evaluate options, and propose effective solutions.
• •Business Acumen: Applies knowledge of MPC’s business, industry and the marketplace to advance the organization’s goals. Makes decisions and recommendations clearly linked to MPC’s strategy.
• •Continuous Improvement Mindset: Identifies and leads opportunities for continuous improvement and value creation, both incremental and large scale.
• •Data Quality Assurance: Data Quality Assurance is any systematic process of checking to see whether organizational data that is being developed is meeting specified requirements in terms of relationships, standards, structure and content.
• •Decision Support Analytics: Leverages analytics, reporting, and decision frameworks to generate insights, evaluate alternatives, and support informed, data-driven decision-making.
• •Decision Support System: Implements and optimizes digital tools and planning systems to support modeling, analytics, and decision processes, ensuring scalability, usability, and alignment with business needs.
• •Influencing Others: The ability to garner support for initiatives by gaining the respect of others and inspiring trust and confidence.
• •Integrated Planning & Optimization: Designs and manages integrated planning processes that align supply, demand, and operational constraints to optimize value across the enterprise value chain.
• •Model Governance: Establishes and enforces standards for data, models, and assumptions to ensure accuracy, consistency, transparency, and reliability of planning and decision outputs.
• •Optimization Modeling: Applies optimization models and quantitative techniques to evaluate scenarios, improve system performance, and support complex operational and commercial decisions.
• •Problem Solving: Problem Solving is a step-by-step process of defining a problem, searching for information, and testing a series of solutions until the problem is solved. It involves critical thinking, analysis and persistence
• •Project