Senior Analyst Advanced Analytics, Commercial Analytics

The Estée Lauder Companies Inc.; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

The Estée Lauder Companies Inc. is seeking a Data & AI Product Manager for Digital to lead strategy, development, and evolution of enterprise data, analytics, and AI products that empower faster, more informed business decisions across the organization.

You will drive foresight capabilities, analytics infrastructure, and AI-powered insights to transform reporting into proactive decision support. You will own end-to-end product ownership across discovery to lifecycle management, collaborating

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.

Responsibilities

• 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.
• Drive evolution of AI-enabled insights, conversational analytics, automated insights, anomaly detection, and intelligent harmonization.
• 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.
• Create alignment around product vision, priorities, scope, success measures, and roadmap.

Skills

Data strategy

Analytics product management

Executive stakeholder management

Product roadmap

AI-enabled products

Business intelligence

Confluent analytics

Tools

GCP

Databricks

SQL

Python

Looker

Power BI

Job 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.

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.