VP Global Data Science & AI

Valvoline Global•Cincinnati, OH

1d•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. The role 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 position owns an annual operating budget of more than $10 million and $2 million in annual capital investment, with responsibility spanning operations in more than 100 countries and an enterprise with more than $3.5 billion in annual revenue. The position is based in a climate-controlled corporate office, with an expectation of working in the Cincinnati office four days per week. 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.
Experience with supply chain analytics across planning, inventory, manufacturing, logistics, procurement, supplier performance, and network optimization.
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.
Ability to work at a computer for extended periods and attend virtual and in-person meetings.
Occasional standing, walking, or light lifting of up to 10 pounds may be required for presentations or workshops.

Nice To Haves

MBA or other advanced degree.
SAP Analytics Cloud experience.
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 equipping 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.
Strengthen decisions by acquiring relevant third-party market and industry data and integrating it into analytical and AI models.
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.
Set the enterprise AI strategy and investment portfolio, focusing resources on the highest-value opportunities.
Convert priority AI opportunities into adopted solutions that improve agreed business outcomes, including costs, productivity, working capital, forecast accuracy, and 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, and stop initiatives that fail 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, delivering services and advancing 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.