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About This Role
### Position
Louis Vuitton Americas is seeking a Senior Data Scientist – Analytics and AI Data to join our Digital team based in New York City. This individual will act as a hybrid data scientist, MLOps engineer, BI SME, business analyst, and data engineer, while owning the roadmap of analytics and AI solutions that can be industrialized and scaled by central teams. You will sit at the intersection of local business needs and global data/AI strategy, ensuring solutions are impactful, scalable, and aligned with enterprise priorities.
### Job responsibilities
- Product Strategy \& Cross\-Functional Roadmap:
+ Lead the comprehensive analytics and AI roadmap, guiding it from opportunity identification and prioritization through delivery and transition to central teams.
+ Collaborate with diverse functional stakeholders (e\-commerce, retail, CRM, supply chain, finance, etc.) to identify use cases, define problem statements, and translate these into clear data product requirements.
+ Establish success metrics, measure value realization, and continuously refine the roadmap based on impact and feedback.
- Data Science \& Advanced Analytics:
+ Design and lead predictive models and analytical solutions utilizing Python and SQL.
+ Conduct thorough exploratory data analysis, feature engineering, model training, validation, and comprehensive documentation.
+ Develop reusable analytical assets (scores, segments, KPIs) for broad application across various markets and functions.
- MLOps \& Data Engineering:
+ Collaborate with MLOps and Data Engineering teams to develop, orchestrate, and maintain robust data and machine learning pipelines, leveraging tools such as Dataiku and Google BigQuery.
+ Implement best practices for testing, version control, monitoring, and observability to ensure the creation of reliable, production\-grade solutions.
+ Partner with central data engineering and platform teams to align initiatives with enterprise standards and promote scalability.
+ Work alongside business, local, and central Data Engineering teams to facilitate data integration and validation, effectively addressing diverse business challenges.
- BI, Insights \& Business Analysis:
+ Act as a Business Intelligence Subject Matter Expert, contributing to the definition of semantic layers, Key Performance Indicators (KPIs), and data models that enable self\-service analytics.
+ Guide or develop insightful dashboards and reports using tools like Power BI, Tableau, or Looker, ensuring their actionability and broad adoption across the organization.
+ Perform business analysis, meticulously gathering requirements, mapping processes, and transforming analytical findings into clear recommendations and compelling narratives for various stakeholders.
- Collaboration with Central / Global Teams:
+ Prepare and package local or cross\-functional solutions, including code, design specifications, documentation, and KPIs, for industrialization and ongoing maintenance by central teams.
+ Contribute to the continuous evolution of global data and AI standards, reference architectures, and best practices.
+ Represent cross\-functional analytics and AI needs in global forums, effectively highlighting dependencies, potential risks, and opportunities for scaling solutions.
### Profile
We are excited to speak to skilled and experienced data professional with 3\+ years of hands\-on experience in data science, analytics, or data engineering, demonstrating end\-to\-end ownership of data products. The ideal candidate will possess strong proficiency in Python (pandas, NumPy, scikit\-learn), advanced SQL skills, and experience with cloud data warehouses such as Google BigQuery. Practical experience with enterprise data processing platforms like Dataiku is also essential. Candidates should have demonstrated expertise in at least three of the following areas: data science/machine learning, BI/reporting and metric design, business analysis/stakeholder management, data engineering/data processing, or Gen AI/Agentic AI product deployment. Strong communication and storytelling abilities are crucial for fostering collaboration between business and technical teams.
Preferred qualifications include experience in retail, luxury, fashion, or consumer environments, familiarity with experimentation (A/B testing), customer analytics (segmentation, propensity, churn, LTV), or marketing attribution. Exposure to Generative AI/LLMs and their application to customer experience and productivity, along with experience in a hub\-and\-spoke operating model, are also highly valued. Core competencies for this role include transversal product thinking, technical breadth spanning data science, MLOps, BI, data analysis, and data engineering, the ability to lead initiatives effectively across functions and geographies, and a strong outcome orientation focused on measurable business value and long\-term maintainability.
The appointed candidate will be offered a base salary within the range USD $140,000 \- $160,000, a comprehensive benefits package including: medical, dental, vision, short and long\-term disability, various paid time off programs, employee discount/perks and two retirement plans both with employer contributions.
- Please note that restrictions may apply to part\-time employees
*The position requires 4 days per week on site in the Louis Vuitton Corporate Office, located in the heart of Midtown, Manhattan.*
### Additional information
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.
In addition to a generous benefits package, unparalleled career development opportunities, both locally \& globally, as an employee at Louis Vuitton, you can expect to be provided with industry leading training which will offer you an in\-depth insight into the luxury and retail environment.
*LOUIS VUITTON*
MAISON
------------------------------
Founded in Paris in 1854, Louis Vuitton perpetuates the ambitious vision of its namesake. From his origins as a master trunk maker, manufacturing boxes used to pack both everyday objects as well as voluminous wardrobes, Louis Vuitton and his successors introduced numerous innovations including the advent of the flat\-top trunk, lightweight canvas, signature patterns, and the tumbler lock. Today, Louis Vuitton’s legacy is expressed through its rigorous spirit of innovation, the boldness of its creations and an uncompromising demand for excellence.
Salary Context
This $140K-$160K range is below the median for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Louis Vuitton, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 27% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Louis Vuitton AI Hiring
Louis Vuitton has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Jose, CA, US, New York, NY, US. Compensation range: $83K - $215K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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