Refining Reliability Analytics Engineer

An exciting career awaits you At MPC, we’re committed to being a great place to work – one that welcomes new ideas, encourages diverse perspectives, develops our people, and fosters a collaborative team environment. POSITION SUMMARY: The Refining Reliability Analytics Engineer develops, deploys, and sustains analytical capabilities that improve the reliability, availability, and performance of refinery equipment and process systems. The position operates at the intersection of reliability engineering, refinery process analysis, statistics, machine learning, and industrial data analytics. The engineer combines process historian, condition-monitoring, equipment-performance, maintenance, inspection, and operating-context data to identify abnormal behavior, emerging degradation, and changes in asset or process performance. The role requires hands-on development of analytical models and sufficient engineering understanding to ensure that model inputs, relationships, and outputs are technically credible and operationally meaningful. Core applications include rotating-equipment health and performance analytics for pumps, compressors, turbines, motors, and associated auxiliary systems; process-unit and reactor analytics, including weighted average bed temperature (WABT) monitoring for hydrotreaters; anomaly detection and early identification of equipment degradation, process deviation, instrumentation concerns, and changing operating relationships; and reusable analytical methods that scale across comparable assets, process units, and refinery sites. The Reliability Analytics Engineer works with process engineers, rotating-equipment specialists, reliability engineers, operations personnel, maintenance professionals, data scientists, data engineers, and IT teams to move analytical concepts from exploratory analysis into validated and sustainable Asset Health Monitoring (AHM) capabilities. KEY RESPONSIBILITIES: Reliability and Process Analytics Translate equipment failure modes, process-degradation mechanisms, and refinery operating concerns into observable variables, analytical features, and detection methods. Develop, validate, deploy, and sustain statistical, machine-learning, and engineering-based models for equipment and process health. Apply change-point detection, statistical process control, dynamic baselining, multivariate analysis, residual monitoring, classification, regression, and time-series methods. Combine operating conditions, equipment configuration, process loading, maintenance history, and instrumentation quality to interpret model results. Design explainable outputs that identify the abnormal condition, contributing variables, operating context, confidence, and reason engineering review is warranted. Validate outputs with engineering subject-matter experts using known events, operating history, retrospective analysis, and false-positive and false-negative reviews. Analytics Integration & Application Translate AHM data models and predictive algorithms into practical reliability strategies that support enterprise initiatives. Support development of KPIs and dashboards that measure equipment health, RAM performance, and intervention effectiveness. Partner with engineers, analysts, and data scientists to validate models and ensure outputs align with engineering and operational realities. Collaboration & Training Work collaboratively with refinery teams and site-level engineering/operations organizations. Develop and deliver training to refine site staff on reliability concepts, AHM tools, and best practices. Foster a culture of continuous improvement and knowledge-sharing. Continuous Improvement & Benchmarking Monitor industry trends, regulatory requirements, and emerging technologies in reliability engineering and asset monitoring. Benchmark against peers and incorporate lessons learned into AHM program enhancements. Education and Experience Bachelor's degree in engineering, computer science, data science, statistics, applied mathematics, operations research, or a related technical discipline. Significant experience developing advanced analytical solutions in refining, petrochemical, energy, manufacturing, or another asset-intensive industrial environment. Demonstrated experience applying statistical modeling, machine learning, anomaly detection, predictive analytics, or time-series analysis to industrial operating data. Demonstrated ability to connect analytical methods with equipment behavior, process relationships, degradation mechanisms, or reliability outcomes. Advanced proficiency in Python or R and working knowledge of SQL. Experience combining time-series, operating, equipment, maintenance, or other industrial datasets and validating findings with engineering or operational SMEs. Ability to communicate analytical findings, uncertainty, limitations, and operational relevance to technical and nontechnical audiences. Willingness to travel up to 25% to support refinery sites. Key Skills & Attributes Experience developing health and performance analytics for pumps, compressors, turbines, motors, or associated auxiliary systems. Experience working with rotating-equipment vibration, temperature, pressure, flow, speed, load, efficiency, lubrication, seal, and maintenance data. Experience identifying rotating-equipment anomalies using change-point detection, statistical process control, multivariate analysis, residual modeling, or dynamic baselines. Experience performing hydrotreater or catalyst-bed reactor analysis using WABT, individual-bed temperatures, thermocouple profiles, temperature rise, process rates, pressure, hydrogen relationships, quench behavior, or catalyst age. Understanding of reliability principles, failure modes, predictive maintenance, condition-based monitoring, RAM, RCFA, and reliability-centered maintenance. Experience developing, deploying, monitoring, and sustaining analytical models in production environments. Experience with industrial historian and analytics platforms such as PI and Seeq and with cloud, API, visualization, or model-operationalization technologies. PREFERRED EXPERIENCE: Bachelor’s Degree in relevant quantitative field required. Master’s Degree or PhD in Computer Science, Statistics, Mathematics, or Operations Research preferred. Six (6) years or more of relevant experience required. As an energy industry leader, our career opportunities fuel personal and professional growth. Location: Findlay, Ohio Additional locations: Job Requisition ID: 00023795 Location Address: 539 S Main St Education: Employee Group: Full time Employee Subgroup: Regular Marathon Petroleum Company LP is an Equal Opportunity Employer and gives consideration for employment to qualified applicants without discrimination on the basis of race, color, religion, creed, sex, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), sexual orientation, gender identity, gender expression, reproductive health decision-making, age, mental or physical disability, medical condition or AIDS/HIV status, ancestry, national origin, genetic information, military, veteran status, marital status, citizenship or any other status protected by applicable federal, state, or local laws. If you would like more information about your EEO rights as an applicant, click here . If you need a reasonable accommodation for any part of the application process at Marathon Petroleum LP, please contact our Human Resources Department at . Please specify the reasonable accommodation you are requesting, along with the job posting number in which you may be interested. A Human Resources representative will review your request and contact you to discuss a reasonable accommodation. Marathon Petroleum offers a total rewards program which includes, but is not limited to, access to health, vision, and dental insurance, paid time off, 401k matching program, paid parental leave, and educational reimbursement. Detailed benefit information is available at .The hired candidate will also be eligible for a discretionary company-sponsored annual bonus program. Equal Opportunity Employer: Veteran / Disability We will consider all qualified Applicants for employment, including those with arrest or conviction records, in a manner consistent with the requirements of applicable state and local laws. In reviewing criminal history in connection with a conditional offer of employment, Marathon will consider the key responsibilities of the role.