Cartier Industrial Methods Data Analyst
- Location
- Seniority
- Mid-Level
- Posted
- Mar 30, 2026
About Cartier
Cartier is an iconic maison of high jewellery and watchmaking, operating within the luxury goods sector and part of a major luxury group. The brand combines artisanal craftsmanship with international reach, offering employees exposure to heritage-led design, technical manufacturing processes and cross‑disciplinary collaboration across ateliers, product and retail teams.
Cartier in Paris seeks an Industrial Methods Data Analyst to drive manufacturing analytics, process improvement and dashboarding for production teams.
Role & Responsibilities
- Collect, clean and validate production and methods data from shop‑floor systems and engineering records to ensure high data integrity.
- Design and maintain dashboards, KPI frameworks and regular reporting to monitor process performance, yield, cycle times and quality indicators.
- Perform statistical analyses and root‑cause investigations to identify process inefficiencies and drive continuous improvement initiatives.
- Develop predictive models and data‑driven tools to support industrial methods, capacity planning and process standardisation.
- Collaborate with industrial engineers, production managers and quality teams to translate analytical insights into operational actions and control plans.
- Support deployment of digital tools and data pipelines; document methodologies, assumptions and deliverables for cross‑functional stakeholders.
Qualifications
- Bachelor's degree in Industrial Engineering, Manufacturing Engineering, Data Science, Statistics or a related technical discipline; Master's preferred.
- Proven experience in data analysis within an industrial or manufacturing environment, ideally in luxury goods, watchmaking or precision production.
- Hands‑on proficiency with SQL and data manipulation; demonstrated experience with Python or R for statistical analysis.
- Advanced skills in Excel and experience building dashboards with Power BI or Tableau.
- Working knowledge of process improvement methodologies (Lean, Six Sigma) and statistical process control (SPC).
- Strong communication skills with the ability to present technical findings to operational and executive stakeholders.
Skills
Experience
Typically requires 3–5 years of applied data analysis experience in a manufacturing or industrial engineering context, with hands‑on involvement in process improvement projects and cross‑functional deployment of analytical solutions.
Education
Bachelor's degree in Industrial Engineering, Manufacturing Engineering, Data Science, Statistics or related field; Master's degree preferred.
Workplace
This position is based in Paris, Île-de-France, France.
Culture
Cartier fosters a culture rooted in craftsmanship, precision and aesthetic excellence where technical expertise is valued alongside creative sensibility. Teams operate in a collaborative, multi‑disciplinary environment that blends artisanal workshop traditions with modern industrial and digital practices.
About Cerulean
Cerulean is the definitive career portal for the global luxury industry. We match exceptional professionals with exclusive opportunities at the world's most prestigious brands. From haute couture and fine watchmaking to prestige beauty, hospitality, and boutique retail, Cerulean centralises luxury employment to help you find the career for which you were destined.
Frequently Asked Questions
The luxury industry is characterised by a diverse and nuanced nomenclature. Esteemed houses frequently employ proprietary terminology, and even within a single organisation like Cartier, titles may vary across global markets to reflect local conventions. To ensure absolute clarity, Cerulean assigns a standardised, industry-coherent canonical title to every listing. However, it is worth noting that this role is functionally synonymous with «Industrial Data Analyst», «Manufacturing Data Analyst», «Production Data Analyst», «Process Data Analyst», and other variations. Our sophisticated search architecture anticipates these variations, ensuring that inquiries using related terms will seamlessly yield the exact roles you desire.