Vice President, Data & Analytics

VF Corporation•Denver, CO

1d•$252,000 - $315,000

About The Position

The Vice President, Data & Analytics is the enterprise leader responsible for defining and owning VF’s data, analytics, and AI strategy. This role is accountable for driving measurable business outcomes and competitive advantage through the effective use of data and AI across consumer, product, and supply chain domains. Operating in a highly federated environment, this leader establishes the enterprise operating model, governance, and standards for data and analytics, ensuring scalable, trusted, and business-aligned capabilities across brands and regions. As a key member of the Digital and Technology leadership team, the VP partners closely with business and brand leaders to shape priorities, influence decision-making, and embed data and AI-driven thinking into core processes. This role champions responsible AI, data governance, and compliance, while translating emerging technologies into practical, scalable solutions that deliver sustainable business impact.

Requirements

15+ years in technology with 8+ years leading enterprise Data, Analytics, and AI functions, with a proven track record of delivering business-aligned strategy and measurable outcomes.
Deep expertise in data science, machine learning, statistical modeling, experimentation (A/B testing), and decision intelligence, with experience operationalizing models via MLOps.
Experience industrializing generative and agentic AI, including LLMs and LLMOps, to deliver scalable, AI-driven insights and business value.
Strong experience designing and scaling modern data platforms and AI-powered data engineering (e.g., lakehouse, streaming, DataOps) through high-performing teams.
Proven ability to operationalize insights through KPIs, dashboards, and embedded, self-service, and conversational analytics.
Strong architecture leadership with experience defining enterprise-grade target architectures and delivering associated roadmaps across data platforms, models, and visualization.
Expertise in data governance, security, cataloging, lineage, and responsible AI, with hands-on experience using enabling tools.
Experience leading cloud-based data and AI ecosystems including Azure, AWS, GCP, Databricks, Snowflake, and BI platforms such as Power BI, Tableau, and Looker.
Demonstrated success building and leading large, globally distributed, multidisciplinary teams.
Strong business partnership skills, with the ability to influence senior stakeholders, frame high-value opportunities, and drive ROI-focused outcomes.
Experience guiding vendor selection, platform investment decisions, and operating model design.
Bachelor's or master's degree in computer science, data science, statistics, engineering, or related field (or equivalent experience).

Nice To Haves

Industry experience in consumer products (apparel, footwear, and/or accessories) is preferred.

Responsibilities

Define and execute the enterprise Data & Analytics strategy across data management, AI-powered data engineering, governance, platforms, advanced analytics, and data science, aligned to business objectives and measurable value.
Build and scale a global data science and AI practice spanning predictive/prescriptive modeling, machine learning, experimentation, decision intelligence, and generative/agentic AI (including LLMs), delivering measurable outcomes across the business.
Industrialize AI-driven insights by leveraging LLMs, agentic AI, and self-service/conversational analytics, embedding trusted insights directly into business processes and decision-making.
Drive enterprise adoption of data and analytics by translating complex insights into clear, decision-ready narratives and embedding analytics into core commercial and operational workflows.
Partner with business leaders to shape a value-driven portfolio of data and AI use cases, prioritizing high-impact opportunities and ensuring delivery of measurable ROI, growth, efficiency, and automation.
Modernize and scale AI-powered data engineering and DataOps, including automated pipelines, data quality, metadata, and cataloging, to enable AI-ready, reusable, and cost-efficient data products.
Define and deliver modern, enterprise-grade data and AI architecture, including platforms, models, MLOps/LLMOps, and visualization, supported by a clear, prioritized roadmap.
Establish enterprise standards across the data and AI lifecycle, including governance, security, and responsible AI, ensuring scalable, resilient, and future-ready capabilities.
Lead, mentor, and scale a global, multidisciplinary team of data engineers, data scientists, ML/AI specialists, and analytics professionals, establishing effective operating models and ways of working.
Act as a trusted advisor and thought leader to senior stakeholders, driving adoption of data- and AI-driven decision-making across brands and functions.