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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.
Description
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Position Summary
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The Vice President, Value Chain Data \& AI Product, drives the design, delivery, and measurable business impact of data and AI products across the global value chain, including demand and supply planning, manufacturing, procurement, logistics, quality, and operational sustainability. This executive is accountable for scaling advanced analytics, forecasting, and ML\-driven decision intelligence to improve resilience, service levels, efficiency, and cost performance across end\-to\-end operations.
The VP leads a global product organization responsible for delivering scalable, reusable data and AI capabilities that enable supply chain execution, optimization, and scenario\-based decision\-making. All initiatives are tied to operational KPIs, cost and service outcomes, and enterprise value creation.
This role serves as the primary Data \& AI partner to the Chief Value Chain Officer (CVCO) and value chain leadership, translating complex operational challenges into enterprise\-grade data and AI products that drive measurable performance improvement.
Description
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Strategic Leadership \& Partnership:
- Define and own the multi\-year Value Chain Data \& AI product vision and roadmap, aligned to global supply chain, operations, and sustainability priorities.
- Serve as the primary Data \& AI partner to the Chief Value Chain Officer (CVCO) and value chain global leadership teams.
- Act as the data and AI owner for the end\-to\-end value chain operations, including:
+ Demand and supply forecasting, scenario planning, and deployment analytics
+ Manufacturing performance, capacity, yield, and quality intelligence
+ Procurement, vendor performance, and cost optimization analytics
+ Inventory, logistics, fulfillment, and service\-level decision intelligence
+ Operational sustainability and EHS analytics
- Maintain fluency in the value chain technology landscape (e.g., SAP S/4HANA, Kinaxis, Anaplan, MES), while partnering with system/platform owners to ensure data readiness and integration.
- Represent Value Chain Data \& AI priorities in enterprise forums, governance bodies, and investment reviews to align business and data strategies globally.
- Translate value chain data and AI strategies into defined OKRs, operational KPIs, financial impacts, and P\&L outcomes.
- Collaborate with emerging technologies teams to assess and introduce new ML, optimization, and decision\-intelligence capabilities into the value chain roadmap.
Description (Cont.)
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Product Portfolio \& Delivery Oversight:
- Lead the end\-to\-end delivery of the Value Chain Data \& AI product portfolio, ensuring solutions are designed for scalability, reuse, and cross\-functional integration across global operations.
- The product portfolio spans:
+ Planning \& Forecasting Intelligence \- demand and supply modeling, scenario planning, deployment analytics, and operational visibility.
+ Manufacturing \& Quality Intelligence \- production performance, capacity and yield analytics, quality and safety insights, and plant\-level optimization.
+ Procurement \& Vendor Intelligence \- supplier performance, cost optimization, and sourcing analytics.
+ Logistics \& Fulfillment Intelligence \- inventory optimization, distribution, service\-level performance, and network visibility.
+ Operational Sustainability \& EHS Intelligence \- sustainability metrics and EHS analytics.
- Oversee prioritization, funding, and execution and measure impact across global and regional value chain roadmaps
- Own the end\-to\-end data architecture and data quality strategy for value chain datasets, ensuring availability, trust, and readiness.
- Oversee the development, deployment, and lifecycle management of forecasting engines, optimization tools, agent\-based automation, dashboards, and other data products
- Partner with the Build Organization (Engineering, AI/ML, Platforms) to align technical architecture, secure resourcing, ensure delivery excellence, and drive speed\-to\-value.
Stakeholder Engagement \& Change Leadership
- Serve as the Data \& AI representative within Value Chain leadership forums, guiding priorities and investments.
- Drive measurable adoption across regional supply chain, manufacturing, procurement, and logistics teams, ensuring data and AI solutions are embedded into daily planning, execution, and decision workflows.
- Foster cross\-functional collaboration with Finance, Technology, Operations, and Sustainability partners to align analytics and AI initiatives to enterprise priorities.
- Champion education, enablement, and operating model change to increase data\-driven decision\-making across value chain functions.
- Advocate for responsible, ethical, and transparent AI usage in operational, forecasting, and optimization use cases.
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 data, analytics, product management, or technology leadership roles within supply chain, operations, or manufacturing environments.
- Deep understanding of end\-to\-end value chain ecosystems, including demand and supply planning, manufacturing operations, procurement, logistics, quality, and operational sustainability.
- Expertise in advanced analytics and ML use cases such as forecasting, scenario modeling, optimization, inventory planning, and operational performance analytics.
- Technical fluency in data platforms, integration frameworks, and modern cloud\-based architectures (e.g., Azure, Databricks).
- Experience building and leading global teams and driving cross\-functional digital, data, or AI transformation initiatives across operations and supply chain functions.
- Proficiency in Agile and product\-centric delivery methodologies.
- Strong business acumen with experience delivering measurable improvements in service levels, cost performance, resilience, and operational KPIs through data and AI solutions.
Pay Range:
The anticipated base salary range for this position is $211,600\.00 to $377,200\.00. Exact salary depends on several factors such as experience, skills, education, and budget. Salary range may vary based on geographic location. In addition to base salary, this position is eligible for participation in a highly competitive bonus program as well as participation in the share incentive plan. In addition,
In addition to base salary, this position is eligible for participation in a highly competitive bonus program with the possibility for overachievement based on performance and company results. 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, paid leave programs, education\-related programs, paid holidays and vacation time, and many others. Many of these benefits are subsidized or fully paid for by the company.
Equal Opportunity Employer
It is Company's policy not to discriminate against any employee or applicant for employment on the basis of race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth and related medical conditions), gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, protected medical condition as defined by applicable state or local law, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances. The Company will endeavor to provide a reasonable accommodation consistent with the law to otherwise qualified employees and prospective employees with a disability and to employees and prospective employees with needs related to their religious observance or practices. Should you wish to apply for this position or any other position with the Company and you believe you require assistance to complete an application or participate in an interview, please contact [email protected].
Michigan Applicants: Persons with disabilities needing accommodations for employment must notify the company in writing of the need for an accommodation within 182 days after the date the person with a disability knew or reasonably should have known that an accommodation was needed.
Philadelphia Applicants: Philadelphia's Fair Chance Hiring Law
Rhode Island Applicants: The company is subject to chapters 29\-38 of title 28 of the general laws of Rhode Island and is therefore covered by the state's workers' compensation law.
Salary Context
This $211K-$377K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($294K) sits 37% above the category median. Disclosed range: $211K to $377K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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: $377K - $377K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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