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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Operational Intelligence, including the development and governance of analytics strategies, predictive modeling, and optimization solutions. Proven ability to lead cross-functional teams and drive measurable business outcomes through data-driven decision-making.
Highest-signal resume keywords
Progressive Leadership ExperiencePredictive Analytics DevelopmentIndustrial Analytics ExpertiseData Quality CommitmentOperational Technology Knowledge
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Predictive ModelingStatistical MethodsOptimization TechniquesData ScienceProcess IntelligenceAI-Enabled SolutionsIndustrial Time-Series DataAsset ModelingGovernance StandardsDecision-Support Practices
Soft Skills
Problem-SolvingDecision-MakingCommunication SkillsInfluence LeadershipTrust Building
Tools & Technologies
PI SystemAVEVAMQTTSparkplugISA-95Unified Namespace
Certifications & Qualifications
Bachelor’s Degree in EngineeringBachelor’s Degree in Data ScienceBachelor’s Degree in Computer ScienceBachelor’s Degree in StatisticsBachelor’s Degree in Applied MathematicsBachelor’s Degree in Industrial EngineeringBachelor’s Degree in Chemical Engineering
Industry Keywords
RefineryManufacturing TechnologyProcess OptimizationIndustrial Chemical ProcessingFood ManufacturingContinuous Process EnvironmentBatch Process Environment
Tech Stack
Tools & technologiesCloudCyber Security
About the role
Key responsibilities & impact- Provide enterprise leadership and capability ownership for the Operational Intelligence discipline across refinery sites
- Develop and maintain the Operational Intelligence strategy, standards, technology roadmap, architecture, and governance model
- Own the enterprise Operational Context Standard, including asset models, measurement vocabulary, semantic classification, engineering units, naming conventions, and metadata quality expectations
- Establish and chair the Operational Intelligence governance forum
- Lead, coach, and develop the central team and build a sustainable talent pipeline
- Partner with refinery and functional leaders to identify and prioritize high-value operational problems
- Translate operational needs into a governed portfolio of intelligence initiatives with objectives, ownership, value measures, and adoption plans
- Lead development, deployment, and sustainment of performance analytics, predictive capabilities, optimization opportunities, decision-support tools, and operational performance models
- Define and own the production lifecycle for deployed intelligence solutions, including monitoring, model-drift detection, failure response, and retirement criteria
- Own the evaluation standard for analytical and AI outputs, including held-out validation, accuracy, error, and fault-rate measurement
- Establish the enterprise position on analytics compute relative to OT and IT boundaries and approved third-party/cloud AI services
- Own build-versus-buy standards, evaluate vendors, run proofs of value, and manage supplier relationships
- Build working relationships across Operations, Engineering, Maintenance, Reliability, EIC, Controls, Infrastructure, Cybersecurity, Information Technology, Finance, and other functions
- Ensure analytics and AI initiatives meet data, security, architecture, lifecycle, validation, documentation, and governance expectations
- Establish methods to measure operational and financial value, with Finance-verified post-implementation benefits
- Guide responsible use of artificial intelligence within Operational Intelligence
- Lead the Operational Intelligence Community of Practice
- Provide technical and business guidance for data quality, operational context, model performance, solution sustainment, and user adoption
- Coordinate with EIC, Controls, and OT Infrastructure within defined discipline boundaries
- Communicate portfolio performance, risks, decisions, business value, and roadmap progress to senior leadership and refinery stakeholders
- Travel to manufacturing sites, vendor facilities, and project locations as required
Requirements
What you’ll need- Progressive leadership experience in industrial analytics, data science, process intelligence, operational technology, manufacturing technology, process optimization, engineering, or a related discipline
- Demonstrated experience leading or mentoring data scientists, analytics professionals, engineers, or cross-functional technical teams
- Experience converting manufacturing or operational problems into analytic use cases and measurable business outcomes
- Experience developing, deploying, validating, and sustaining predictive analytics, optimization, decision-support, or AI-enabled solutions in a production operating environment
- Experience with industrial time-series data at scale, including PI or AVEVA, MQTT and Sparkplug, or equivalent, and ISA-95 or Unified Namespace style asset modeling
- Experience defining data and context requirements and holding platform owners accountable to them
- Experience combining statistical methods with first-principles process understanding, including mass and energy balance, thermodynamics, and reaction or crystallization behavior is strongly preferred
- Experience working with operational data and Operations, Engineering, Maintenance, Reliability, Controls, and Information Technology teams
- Experience establishing standards, roadmaps, governance, reusable methods, and a prioritized portfolio of analytics initiatives
- Experience in refinery, milling, commodity processing, industrial chemical processing, food manufacturing, or another continuous or batch process environment is preferred
- Bachelor’s degree in Engineering, Data Science, Computer Science, Statistics, Applied Mathematics, Industrial Engineering, Chemical Engineering, or a related technical field from an accredited university
- Strong knowledge of industrial analytics, statistical methods, predictive modeling, optimization, artificial intelligence, and decision-support practices
- Ability to understand manufacturing processes, operational constraints, data context, and requirements for deploying solutions in operating environments
- Ability to lead a small expert team while shaping strategy, portfolio priorities, stakeholder relationships, and technical quality
- Strong governance, risk management, problem-solving, prioritization, and decision-making skills
- Excellent written and verbal communication skills
- Demonstrated ability to lead through influence, build trust, and drive adoption in a matrixed organization
- Commitment to data quality, model validation, cybersecurity, reliability, responsible AI, documentation, and sustainable solution ownership
- Willingness and ability to travel across manufacturing sites and other business locations as required
Benefits
Comp & perks- Equal opportunity employment policy
- Travel to manufacturing sites, vendor facilities, and project locations as required