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About This Role
The Estée Lauder Companies Inc. is one of the world’s leading manufacturers, marketers, and sellers of quality skin care, makeup, fragrance, and hair care products, and is a steward of luxury and prestige brands globally. The company’s products are sold in approximately 150 countries and territories under brand names including: Estée Lauder, Aramis, Clinique, Lab Series, Origins, M·A·C, La Mer, Bobbi Brown Cosmetics, Aveda, Jo Malone London, Bumble and bumble, Darphin Paris, TOM FORD, Smashbox, AERIN Beauty, Le Labo, Editions de Parfums Frédéric Malle, GLAMGLOW, KILIAN PARIS, Too Faced, Dr.Jart\+, the DECIEM family of brands, including The Ordinary and NIOD, and BALMAIN Beauty.
Position Summary
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The Executive Director, Enterprise Data \& AI Product Lead, drives the design, delivery, and measurable business impact of data and AI products that power our employees and corporate functions like Finance, HR, Legal, Technology, and Communications. This executive is accountable for transforming strategy into execution, building, scaling, and optimizing data, analytics, and AI solutions that enhance workforce productivity, financial performance, enterprise risk management, operational efficiency, and decision intelligence.
The ED leads a global product organization responsible for setting the strategy, defining the roadmap, and delivering high\-impact data and AI capabilities that enable end\-to\-end functional excellence. Every initiative must tie directly to business outcomes, operational effectiveness, and enterprise\-wide KPIs.
In addition to execution excellence, the ED acts as a senior partner to CFO, CPO, General Counsel, and Technology leadership, ensuring data and AI initiatives are aligned with functional strategies, capital allocation priorities, and global process transformation goals. The role requires a blend of strategic product leadership, domain expertise across corporate functions, and technical fluency in enterprise systems and data architectures.
Description
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Strategic Leadership \& Partnership:
- Define and own the multi\-year Enterprise Data \& AI product vision and roadmap, aligned to Finance, HR, Legal, Technology, and corporate priorities.
- Serve as the primary Data \& AI strategic partner to C\-suite functional leaders, embedding intelligence, automation, and predictive insights into enterprise planning and performance processes.
- Act as the data and AI owner for core enterprise functions, spanning:
+ HR: workforce planning, talent intelligence, employee experience, and organizational analytics
+ Finance: forecasting, planning, performance management, and financial risk modeling
+ Legal \& Compliance: contract analytics, policy intelligence, audit and regulatory insights
+ Technology: productivity analytics, automation platforms, service intelligence, and digital workplace enablement
+ Communications: employee engagement intelligence, sentiment analytics, and enterprise communication effectiveness insights
+ Enterprise Operations: KPI frameworks, executive dashboards, and cross\-functional performance analytics
- Maintain fluency in the functional landscape and relevant enterprise platforms such as SAP, ServiceNow, HRIS, financial planning systems, and corporate risk platforms, while partnering with platform owners to ensure data and integration readiness
- Represent Enterprise Functions in enterprise forums, governance bodies, and investment reviews to align business and data priorities globally.
- Translate data and AI strategies into defined OKRs, financial impacts, and P\&L outcomes.
- Collaborate with emerging technologies team to assess opportunities to introduce new capabilities into the Enterprise Function roadmap.
Description (Cont.)
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### Product Portfolio \& Delivery Oversight
- Lead the end\-to\-end delivery of the Enterprise Data \& AI product portfolio, including:
+ Workforce analytics and talent intelligence
+ Financial forecasting, risk modeling, and decision\-support tools
+ Productivity insights, automation platforms, and digital workplace AI
+ Legal/Compliance analytics, policy intelligence, and contract analytics
+ Integrated enterprise dashboards and KPI frameworks
- Oversee prioritization, funding, and execution across global and regional roadmaps.
- Own the end\-to\-end data architecture and data quality strategy for all Enterprise Function datasets, ensuring availability, trust, and readiness for analytics and AI.
- Oversee the development, deployment, and lifecycle management of AI/ML models, forecasting engines, agent\-based automation, dashboards, insights tools, and internal analytics applications.
- Lead and develop a high\-performing team of Data \& AI Product Managers and Data Analytics experts.
- Partner with the Build Organization (Engineering, AI/ML, Platforms) to align technical architecture, secure resourcing, ensure delivery excellence, and drive speed to value.
- Establish processes to measure ROI, adoption, and value creation for all delivered products.
Description (Cont.)
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Stakeholder Engagement \& Change Leadership
- Act as the Data \& AI lead within Finance, HR, Legal, and Technology leadership forums, guiding prioritization across competing demands and global transformation initiatives.
- Strengthen collaboration across Corporate Functions, IT, Security, Strategy, and Regional Operations.
- Champion enterprise\-wide adoption of AI\-enabled tools, decision intelligence, and predictive analytics.
- Lead change management, training, and communication programs that build trust, adoption, and organizational readiness for enterprise AI.
- Serve as an advocate for ethical, compliant, and responsible AI, particularly in workforce, finance, and risk domains.
Governance, Operations \& Performance
- Ensure adherence to enterprise data governance, privacy, and security standards.
- Manage domain budgets, resource allocation, and OKRs in coordination with enterprise strategy and finance teams.
- Establish transparent reporting for leadership, including product performance dashboards, delivery milestones, and realized business impact.
- Partner with Governance, Architecture, and Strategy \& Ops to continuously evolve operating models, tooling, and delivery maturity.
Qualifications
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- 12–15\+ years of progressive experience in enterprise data, analytics, AI, or product leadership roles.
- Deep understanding of corporate function data ecosystems and processes, including HRIS, ERP workforce planning and talent intelligence, financial planning and forecasting, enterprise risk and compliance, productivity, and digital workplace enablement.
- Technical fluency across modern data platforms, cloud architectures, integration frameworks, and automation technologies.
- Experience building and leading global teams and driving cross\-functional digital, data, or AI transformation initiatives.
- Experience owning enterprise data strategy, including data architecture, data modeling, metadata, semantic layers, and data quality frameworks.
- Experience deploying AI at scale across Finance, HR, Legal, or Technology environments.
- Technical fluency in data platforms, integration frameworks, and modern cloud\-based architectures (e.g., Azure, Databricks).
- Proficiency in Agile and product\-centric delivery methodologies.
Pay Range:
Anticipated Base Salary Range $177,100\.00 to $304,500\.00 (Depending on qualifications, skills, experience and/or budget), based on a 40 hour work week (range to be scaled accordingly). In addition, The Estée Lauder Companies offers a variety of benefits to eligible employees, including health insurance coverage (medical, dental, and vision insurance), wellness and family support programs, life and disability insurance, retirement savings plans, education\-related programs, paid holidays and vacation time. In addition, the Company maintains highly competitive incentive compensation programs (role eligibility may vary based on terms of the respective plan(s)).
You may be eligible to participate in the applicable Commission/Bonus Plan, under the plan guidelines in effect at the time of hire. Additional details regarding the commission plan will be provided as part of your onboarding.
Equal Opportunity Employer
We are an equal\-opportunity employer. Minorities, women, veterans, and individuals with disabilities are encouraged to apply. Accommodations for job applicants with disabilities are available on request.
Artificial Intelligence is used to compare and screen an applicant’s resume as against the posted job description.
Salary Context
This $177K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At The Estée Lauder Companies, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($240K) sits 33% above the category median. Disclosed range: $177K to $304K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Estée Lauder Companies AI Hiring
The Estée Lauder Companies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in NY, US. Compensation range: $304K - $304K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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