Ralph Lauren Head of Data Product Management

Company Description
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands. At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.

Position Overview
TheHead of Data Productsplays a pivotal leadership role in advancingRalph Lauren's enterprise Data Strategy, a foundational enabler of the company'sNext Great Chapter: Driveagenda. Ralph Lauren has articulated a multi-year transformation strategy anchored inadvanced technology, data, AI, and analyticsas critical levers for long-term growth and value creation. The company is actively modernizing core platforms, scaling AI-driven capabilities, and embedding data-informed decision-making across its global consumer ecosystem. Ralph Lauren's transformation spans predictive buying, demand forecasting, omnichannel personalization, digital commerce optimization, and elevated customer experiences powered by enterprise-scale data. The brand is leveraging AI and analytics to improve inventory efficiency, enhance product availability, optimize pricing, and enable real-time consumer engagement across markets and channels-capabilities that directly reinforce the company's strategy, which integrates digital platforms with physical retail to deliver seamless, data-informed experiences worldwide. Within this context, the Head of Data Products is responsible for shaping and operationalizing thedata product portfolio that fuels Ralph Lauren's digital operating model, ensuring enterprise data assets are discoverable, trusted, compliant, reusable, and directly aligned to business value and brand elevation priorities. This leader accelerates the company's move toward a truly data-driven, AI-enabled enterprise-supporting strategic goals such as global DTC growth, personalization at scale, creative and operational agility, and continuous innovation across the value chain. The role ensures that data products deliver measurable value, drive operational excellence, and unlock new digital capabilities while acting as the connective tissue between business stakeholders, data engineering, governance, analytics, and AI teams. The ideal candidate thrives at the intersection of strategy, technology, and business value.

Essential Duties & Responsibilities
1. Data Product Strategy & Vision
* Define and champion the enterprise data product strategy, ensuring alignment with business goals, digital transformation priorities, and value roadmaps.
* Develop a multi-year data product portfolio plan, identifying new opportunities, modernization needs, and rationalization paths.
* Establish enterprise standards for evaluating data product investments, including expected value, feasibility, and adoption impact. 2. Business Alignment & Value Realization
* Partner with senior business leaders to translate their priorities into actionable data product roadmaps.
* Define KPIs, success metrics, and ROI models for every data product.
* Ensure data products materially improve decision-making, operational efficiency, omnichannel performance, and AI readiness. 3. Product Leadership & Operating Model
* Build and lead a high-performing Data Product Management team.
* Define and institutionalize the enterprise data product lifecycle--from discovery to design, build, launch, iteration, and retirement.
* Drive consistent, user-centric product design emphasizing reliability, clarity, trust, and accessibility. 4. Enterprise Collaboration & Orchestration
* Partner across the Data & Analytics ecosystem:
* Data Engineering for scalable, performant data architectures.
* Data Governance to embed quality, lineage, compliance, and security.
* Analytics & AI to ensure data products power advanced modeling, automation, and generative AI use cases.
* Business Units to ensure strong adoption and continuous improvement.
* Serve as the alignment hub for priorities, standards, roadmaps, and delivery timelines. 5. Data Product Delivery & Continuous Improvement
* Oversee delivery of high-quality, scalable data products.
* Implement ongoing performance monitoring (availability, quality, usage, satisfaction, business outcomes).
* Champion iteration and innovation through user feedback, usage insights, and emerging technology. 6. Governance, Standards & Risk Management
* Ensure data products comply with enterprise governance, privacy, and regulatory standards.
* Proactively manage data and operational risks in collaboration with legal, compliance, and security teams.
* Promote responsible data usage and ethical AI practices.

Experience, Skills & Knowledge
Required
* 10+ years in data, analytics, product management, or digital transformation.
* Experience building and scalingdata productsor enterprise data platforms.
* Proven ability to partner with senior business executives and drive value.
* Understanding of modern data engineering, architecture, governance, and analytics.
* Strong communication, stakeholder management, and executive storytelling skills. Preferred
* Experience leading global teams.
* Background in complex, regulated environments (e.g., retail, lifestyle brands, consumer goods, pharma).
* Familiarity with cloud-native data stacks and product management methodologies (Agile, Design Thinking, Lean Product). Success Measures
* Adoption and business impact of data products across functions.
* Demonstrated value creation (efficiency, margin improvements, decision velocity, AI enablement).
* Maturity and effectiveness of the data product operating model.
* Cross-functional partnership quality and satisfaction.
* Scalability, performance, and trustworthiness of the data product ecosystem.