Estée Lauder · Estee Lauder

Data & Ai Product Manager

Long Island City · CDI · Publiée il y a 12 jours

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Détails de l’emploi
Contrat
CDI
Temps de travail
Temps plein
Lieu
Long Island City, US-NY, United States
Publiée le
25/08/2026
Description
Description The Data & AI Product Manager, Digital will lead the strategy, development, and evolution of enterprise data, analytics, and AI products that enable faster, more consistent, and forward-looking business decision-making across the organization. This role will help build foresight capabilities, enterprise insights infrastructure, advanced analytics capabilities, and scalable learning systems that future-proof how the organization understands market performance, consumers, and business opportunities. A key focus will be transforming complex and fragmented market performance data into scalable, AI-powered business intelligence capabilities —moving beyond traditional reporting toward automated insights, conversational analytics, intelligent data harmonization, and decision-support experiences. The role will operate at the intersection of business strategy, data, analytics, AI, engineering, and user experience , owning products from strategy and discovery through launch, adoption, optimization, and lifecycle management. Descriptiobn Continued Data & AI Product Strategy • Own the multi-year product vision, strategy, roadmap, and prioritization for strategic enterprise data, analytics, and AI products. • Translate enterprise and Digital priorities into scalable product capabilities that improve how leaders and teams access insights and make decisions. • Identify opportunities to evolve traditional reporting and analytics into AI-powered intelligence and decision-support products . End-to-End Product Ownership • Own the full product lifecycle across discovery, requirements, design, development, testing, launch, adoption, optimization, and transition/decommissioning . • Translate business needs into clear product requirements, user stories, acceptance criteria, and prioritized product backlogs. • Make product trade-offs across business value, user experience, technical feasibility, scalability, cost, and time-to-value. • Partner with engineering and architecture teams to ensure solutions are reliable, scalable, maintainable, and aligned with enterprise technology standards. AI-Powered Insights & Foresight • Drive the evolution of product capabilities including AI-enabled experiences, conversational analytics, automated insight generation, anomaly and opportunity detection, intelligent harmonization, and decision support . • Partner with Data Science, AI, Engineering, and business teams to identify high-value AI use cases and translate them into scalable product capabilities. • Establish appropriate human-in-the-loop workflows, transparency, validation, and monitoring for AI-generated insights. • Continuously evaluate emerging data and AI capabilities and determine where they can create meaningful business value. Business Partnership & Cross-Functional Product Delivery • Create alignment around product vision, priorities, scope, success measures, and roadmap. • Serve as the bridge between business users and technical teams, ensuring products solve meaningful business problems rather than simply deliver technical functionality. • Orchestrate delivery across Product, Data Engineering, Analytics, Data Science/AI, Architecture, UX, business teams, governance functions, and strategic vendors . • Establish clear ownership, decision rights, dependencies, milestones, and escalation paths across complex enterprise initiatives. • Manage strategic vendors and partners where required while maintaining clear internal ownership of product strategy and outcomes. Adoption, Value & Product Performance • Define product success measures spanning adoption, engagement, data quality, reliability, efficiency, user experience, decision impact, and measurable business value . • Drive adoption through stakeholder engagement, enablement, change management, documentation, and continuous product improvement. • Ensure product investments are connected to measurable business outcomes rather than delivery milestones alone. Qualifications • 6+ years of experience across data/analytics product management, digital product management, analytics, data strategy, or related roles , including experience owning complex enterprise products. • 3+ years of direct product management experience preferred, including responsibility for product strategy, roadmap, prioritization, requirements, delivery, and adoption. • Demonstrated experience building or managing enterprise data, analytics, business intelligence, or AI-enabled products . • Strong understanding of modern data and analytics ecosystems, including SQL, cloud data platforms, data pipelines, semantic/data models, APIs, BI platforms, and AI/ML capabilities . • Technical familiarity with GCP, Databricks, SQL, and Python strongly preferred. • Experience with Looker, Power BI, or comparable business intelligence and analytics platforms preferred. • Experience with GenAI, conversational analytics, AI agents, automated insights, or AI-enabled enterprise products strongly preferred. • Strong understanding of data quality, governance, metadata, security, privacy, and responsible AI considerations within enterprise environments. • Demonstrated ability to translate ambiguous business problems and executive decision needs into clear product strategies and scalable technical capabilities. • Strong product judgment with the ability to prioritize across competing business needs, technical constraints, user experience, and long-term scalability. • Experience operating across global, cross-functional, and highly matrixed organizations. • Strong stakeholder management and executive communication skills, with the ability to influence without direct authority. • Highly analytical and comfortable defining and using product metrics to evaluate adoption, performance, and business value. • Strong written and verbal communication skills with the ability to communicate effectively across business, technical, and executive audiences. • Collaborative, curious, resourceful, and comfortable operating in an evolving environment where not all requirements or solutions are known upfront. •

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