VP Global Data Science & AI

Job Purpose

The Vice President, Global Data, Analytics, and AI sets Valvoline Global's enterprise vision, strategy, operating model, and investment priorities for data, analytics, and AI.

This executive has global accountability for data strategy and governance, master data management, data engineering and platforms, business intelligence, advanced analytics and data science, artificial intelligence, MLOps, and enterprise planning and reporting platforms.

The near-term mandate is to accelerate data modernization and connectivity, with Databricks as a core platform; strengthen master data governance and stewardship; deliver trusted SAP S/4HANA reporting; advance enterprise planning through SAP Analytics Cloud; and turn analytics and AI opportunities into measurable business value, with supply chain decision support as one example.

The role leads a global organization of approximately 40–50 professionals and partners closely with business leaders, IT, Enterprise Architecture, Security, Legal, Risk, and Finance.

How You Make an Impact (Job Accountabilities)

Data Strategy, Governance, and Master Data

• Establish an enterprise data strategy and governance model that directs investment, clarifies decision rights, and improves the consistency, availability, security, and trusted use of data.
• Improve the accuracy, control, and day-to-day reliability of customer, product, supplier, and other core business data across the enterprise.
• Increase adoption of data, analytics, and AI by equipping leaders and teams to interpret information, challenge assumptions, and use new capabilities responsibly.

Enterprise Analytics and Decision Support

• Deliver an enterprise analytics and data science portfolio that is widely adopted and produces measurable improvements in decision quality, operating performance, and financial outcomes.
• Provide leaders and teams with trusted business intelligence, dashboards, self-service analytics, and reporting that reduce manual effort and support faster decisions.
• Establish business ownership, intended users, outcome-based plans, service expectations, and lifecycle accountability for critical enterprise data assets.
• Improve forecast quality, working capital, service, cost, and operating performance through decision support across demand planning, inventory, manufacturing, logistics, procurement, network optimization, and integrated business planning.
• Strengthen decisions by acquiring relevant third-party market and industry data and integrating it into analytical and AI models.

Data Engineering, Platforms, and Reporting

• Deliver a secure, reliable, and connected enterprise data platform, including Databricks, that improves access to data and executes standards established by Enterprise Architecture.
• Own SAP Analytics Cloud as the platform for the Latest Estimate financial forecasting tool and ensure timely, accurate, and decision-ready information.
• Deliver reliable SAP S/4HANA reporting that supports operations, financial control, and timely management decisions.
• Manage the cost and value of Databricks and other consumption-based platforms through usage reporting, workload optimization, forecasting, and cost allocation.
• Ensure controlled access to data through enterprise cataloging, lineage, data quality monitoring, and role- and asset-based access controls.

Artificial Intelligence

• Set the enterprise AI strategy and investment portfolio, focusing resources on the highest-value opportunities.
• Convert priority AI opportunities into adopted solutions that reduce costs and improve productivity, working capital, forecast accuracy, growth, or other agreed outcomes.
• Build the capabilities and delivery model to scale AI responsibly across central teams, business functions, strategic partners, and external providers.
• Partner with the AI Governance Council, Legal, Security, Risk, and IT to ensure responsible, compliant AI use. Stop any initiative that fails to meet governance, risk, security, or compliance standards.
• Establish MLOps practices that keep machine learning and generative AI solutions reliable, secure, compliant, and effective in production.
• Improve productivity and business processes through generative AI, intelligent automation, large language model applications, and robotic process automation.

Leadership

• Lead a high-performing global organization of approximately 40–50 professionals, including 4–6 direct reports, that delivers reliable services and advances the company's data, analytics, AI, planning, and reporting priorities.
• Maintain the leadership depth, critical skills, and succession coverage needed for current commitments and future priorities.
• Create a culture of accountability, early risk identification, practical innovation, and effective cross-functional collaboration.
• Align the organization and talent investments with business priorities while retaining critical capabilities and production support across all platforms and solutions.

What You Bring to the Role

• A bachelor's degree in Data Science, Computer Science, Engineering, Business, Economics, or a related field is required; an MBA or other advanced degree is strongly preferred.
• At least 10 years of progressive leadership experience in enterprise data, analytics, data science, master data, or related digital and technology disciplines, including at least five years leading global, multidisciplinary teams at a senior level.
• Experience leading global data strategy, governance, master data, data engineering, connectivity, platforms, business intelligence, reporting, self-service analytics, and data products.
• Experience with enterprise planning and reporting environments and SAP S/4HANA reporting; SAP Analytics Cloud experience is strongly preferred.
• A record of building teams and delivering advanced analytics, data science, and AI solutions through central teams, business functions, and external partners.
• Working knowledge of responsible AI, data quality, model governance, privacy, security, and technology risk management.
• Experience establishing and scaling MLOps, including deployment pipelines, monitoring, model governance, and platform engineering.
• Experience moving generative AI solutions, large language model applications, intelligent automation, or robotic process automation from experimentation into governed business use.
• Experience managing the cost and value of Databricks or a comparable consumption-based cloud platform.
• Experience leading geographically distributed teams and partners across multiple time zones.
• Experience with supply chain analytics across planning, inventory, manufacturing, logistics, procurement, supplier performance, and network optimization.
• Experience in chemicals, lubricants, consumer products, or manufacturing is preferred.
• Executive presence, sound business judgment, and the ability to influence and align leaders across functions and geographies.
• Working knowledge of, and some hands-on proficiency with, Databricks, Unity Catalog, MLflow (or comparable model lifecycle tools), SAP Analytics Cloud, SQL, and Power BI.

Competencies Desired

• Enterprise Leadership – Sets direction, makes clear tradeoffs, and follows through on results.
• Business and Technical Judgment – Connects business priorities with practical data, analytics, and technology choices and makes disciplined investment and resource tradeoffs.
• Influence and Partnership – Builds trust and aligns executives, functional leaders, technical teams, and partners.
• Execution – Moves important work forward while maintaining reliable day-to-day services.
• Global Perspective – Works effectively across functions, countries, cultures, and organizational levels.

Working Conditions / Physical Requirements / Travel Requirements

• Climate-controlled corporate office environment with an expectation of working in the Cincinnati office four days per week.
• Must be able to work at a computer for extended periods and attend both virtual and in-person meetings.
• Occasional standing, walking, or light lifting of up to 10 pounds may be required for presentations or workshops.
• Domestic and international travel of up to 15% may be required, with global travel generally limited to regional hubs.