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Poste
As a Louis Vuitton Data Science Director, you will lead the strategic vision and execution of advanced analytics, machine learning, and AI capabilities. Reporting to the Business Development Director, this role bridges global strategies with local market needs, transforming data into a strategic business asset. You will take a specialized approach to deploy advanced analytics, machine learning, and Generative AI, ensuring our Maison possesses the intelligence required to deliver hyper\-personalized luxury experiences at scale.
Missions
Responsibilities include, but are not limited to:
Strategic Leadership \& Business Partnership
Define and execute the Data Science strategy, aligning global roadmaps with local market priorities to drive revenue growth and operational efficiency.
Act as a key thought partner to leadership, translating complex analytical findings into clear business recommendations and strategic actions.
Drive the identification and deployment of AI\-powered solutions across all LVA departments — including Operations, Merchandising, Finance, HR, Retail, and Client Development — with a focus on eliminating low\-value, repetitive tasks and enabling forecasting, pattern recognition, and data\-driven decision\-making at scale.
Ensure the AI agenda extends beyond any single function, delivering measurable productivity and quality gains for the entire organization.
Maintain active alignment with LVMH Group / LV Central teams to ensure full visibility of Group/Maison\-level AI initiatives, avoiding duplication of effort and leveraging centrally developed assets where available.
Serve as the LVA relay for AI innovation: channel locally identified use cases to Central when relevant, and cascade Group/Maison roadmap priorities into the LVA context.
Innovation \& Data Science Development
Lead the application and scaling of advanced analytics, machine learning, and Generative AI solutions across customer interaction, media optimization, and automated communications.
Serve as the definitive owner for algorithm localization, adapting global AI models to account for local cultural nuances, privacy regulations, and specific consumer behaviors.
Scout and activate emerging use cases (e.g., Agentic AI), ensuring the Maison remains at the forefront of automated commerce and personalized content generation.
Collaborate with IT and Client Development to build a unified data ecosystem, ensuring seamless model integration across all touchpoints.
Team Leadership \& Functional Excellence
Manage, mentor, and evolve a multidisciplinary team, fostering a culture of technical excellence and commercial acumen.
Champion data literacy across the organization, fostering a culture where data is a strategic asset and AI empowers human talent.
Champion an iterative build–test–refine methodology for all AI projects: scope a minimum viable solution, validate with end\-users, measure impact, and incrementally expand — ensuring each cycle is governed by a clear return\-on\-investment framework.
Prioritize and sequence the AI portfolio based on quantified business value, balancing quick wins that demonstrate tangible ROI with longer\-term capability building.
Ensure strong governance across data privacy, security, and lifecycle management practices, adhering to applicable regulations and internal standards.
Profil
Work Experience/Skills
12\+ years in Data Science, AI/ML, or Digital Transformation, with significant experience leading and developing technical teams.
Proven ability to translate technical complexity into commercial value and drive strategic data/AI initiatives in a complex, matrixed environment.
Deep understanding of modern data ecosystems, including cloud, CDP, predictive modeling, LLMs, and Generative AI.
Experience with data governance, privacy, security, and lifecycle management principles.
Exceptional communication, strategic thinking, and organizational leadership skills, capable of influencing senior stakeholders.
Luxury, retail, consumer, or client\-centric industry experience preferred.
Bachelor’s degree in Data Science, Computer Science, or related field required; advanced degree preferred.
The appointed candidate will be offered a salary within USD $200,000 \- $240,000 annually, comprehensive benefits package including: medical, dental, vision, short and long\-term disability, various paid time off programs, employee discounts/perks and two retirement plans both with employer contributions.
Informations complémentaires
Louis Vuitton is a company that respects the uniqueness of each employee and offers everyone the means to find their place and prosper. We promote initiatives aimed at supporting professional equality for everyone. We strive to go above and beyond purely symbolic measures by building a culture passionate about meaningful strategies aimed at crafting an inclusive workforce.
All your tasks are not limited and/or restricted to this job description. You must comply with any reasonable requests from your manager to perform any other duties to fulfill your role's requirements.
Louis Vuitton is an equal employment opportunity employer.
Salary Context
This $200K-$240K 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 Louis Vuitton, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $200K to $240K.
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.
Louis Vuitton AI Hiring
Louis Vuitton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $240K - $240K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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