VP Global Data Science & AI

Valvoline•Charlotte, AL

2d•Onsite

About The Position

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. The role 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, including supply chain decision support. The position leads a global organization of approximately 40–50 professionals, including 4–6 direct reports, and partners closely with business leaders, IT, Enterprise Architecture, Security, Legal, Risk, and Finance. The role has an annual operating budget of more than $10 million and $2 million in annual capital investment. It operates at enterprise scale across more than 100 countries and more than $3.5 billion in annual revenue, with ownership of SAP Analytics Cloud for the Latest Estimate financial forecasting tool and SAP S/4HANA reporting functionality. The work environment is a climate-controlled corporate office, with an expectation of working in the Cincinnati office four days per week. The role involves extended computer work and 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, generally limited to regional hubs.

Requirements

Bachelor’s degree in Data Science, Computer Science, Engineering, Business, Economics, or a related field.
At least 10 years of progressive leadership experience in enterprise data, analytics, data science, master data, or related digital and technology disciplines.
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.
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.
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.

Nice To Haves

MBA or other advanced degree.
SAP Analytics Cloud experience.
Experience with supply chain analytics across planning, inventory, manufacturing, logistics, procurement, supplier performance, and network optimization.
Experience in chemicals, lubricants, consumer products, or manufacturing.

Responsibilities

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 enabling leaders and teams to interpret information, challenge assumptions, and use new capabilities responsibly.
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 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.
Acquire relevant third-party market and industry data and integrate it into analytical and AI models to strengthen decisions.
Deliver a secure, reliable, and connected enterprise data platform, including Databricks, that improves data access and executes Enterprise Architecture standards.
Own SAP Analytics Cloud as the platform for the Latest Estimate financial forecasting tool and ensure timely, accurate, 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 data access through enterprise cataloging, lineage, data quality monitoring, and role- and asset-based access controls.
Set the enterprise AI strategy and investment portfolio, focusing resources on the highest-value opportunities.
Convert priority AI opportunities into adopted solutions that deliver agreed business outcomes, such as reduced costs, increased productivity, improved working capital, forecast accuracy, or growth.
Build capabilities and a 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 and compliant AI use; stop initiatives that do not 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.
Lead a high-performing global organization of approximately 40–50 professionals, including 4–6 direct reports, to deliver reliable services and advance data, analytics, AI, planning, and reporting priorities.
Maintain leadership depth, critical skills, and succession coverage for current commitments and future priorities.
Create a culture of accountability, early risk identification, practical innovation, and effective cross-functional collaboration.
Align organizational structure and talent investments with business priorities while retaining critical capabilities and production support across platforms and solutions.