Richemont Data Scientist Intern
- Location
- MeyrinGenevaSwitzerland
- Employment
- Internship
- Seniority
- Intern
- Department
- IT & Technology Systems
- Posted
- Jun 23, 2026
About Richemont
Richemont is one of the world’s foremost luxury groups, stewarding a distinguished portfolio of maisons across jewellery, watchmaking, fashion and accessories, including Cartier, Van Cleef & Arpels, Jaeger-LeCoultre and Montblanc. As an employer, it combines the heritage of artisanal excellence with a forward-looking culture shaped by innovation, client-centricity and responsible business. Its global teams operate in an environment that values craftsmanship, entrepreneurship and collaboration, offering opportunities to grow within iconic maisons and group functions alike. Richemont attracts professionals who aspire to contribute to enduring luxury, preserving rare savoir-faire while helping define the future of high craftsmanship.
Richemont Data Scientist Intern in Meyrin — develop machine learning models and conduct data analysis within the luxury conglomerate's innovation hub.
Role & Responsibilities
- Develop and implement machine learning models and statistical analyses to support business intelligence initiatives
- Collaborate with cross-functional teams to identify data-driven opportunities and insights
- Conduct exploratory data analysis and create visualizations to communicate findings to stakeholders
- Assist in designing and executing experiments to test hypotheses and validate business assumptions
- Document methodology, findings, and recommendations in clear technical reports
Qualifications
- Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field
- Strong foundational knowledge of machine learning algorithms and statistical methods
- Proficiency in Python and/or R for data analysis and modeling
- Familiarity with data manipulation libraries and tools such as pandas, NumPy, or scikit-learn
- Ability to work collaboratively in a structured team environment
Skills
Experience
["Limited or no professional experience in data science; academic projects and coursework in machine learning or statistics are valued", "Demonstrated ability to apply theoretical knowledge to practical problem-solving", "Experience with real-world datasets or research projects is advantageous"]
Education
["Currently pursuing or recently completed a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or a closely related quantitative discipline"]
Workplace
The successful candidate will be located in Meyrin, Geneva, Switzerland, with easy access to Geneva.
Culture
Richemont fosters a refined, international workplace where its Maisons preserve exceptional craftsmanship while benefiting from the strength and perspective of a global luxury group. As an employer, it values long-term stewardship, collaboration, innovation, and the development of talent within an environment shaped by heritage, creativity, and high standards.
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 Richemont, 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 «Data Science Intern», «Analytics Intern», «Data Analytics Trainee», «Machine Learning Intern», and other variations. Our sophisticated search architecture anticipates these variations, ensuring that inquiries using related terms will seamlessly yield the exact roles you desire.