Data Scientist Intern

Location: MEYRIN

Remote Type: Hybrid

Time Type: Full time

Job Description

The story of Cartier is founded on audacity and passion. For more than 170 years we have embraced a bold, pioneering spirit that continues to inspire our teams across all Métiers from our boutiques to our workshops and corporate offices. Our 9000+ colleagues of 105 nationalities are united by a shared independent spirit and commitment to excellence, striving to continuously enrich our Maison’s heritage by pushing the boundaries of creativity.

We would like to talk to you about the opportunity to join our Maison for an internship as Data Scientist Intern (6 months). As Data Scientist Intern, contribute to the ARIA machine learning project by revamping and enhancing an existing model for short and long-term daily sales prediction. The objective is to update the current outdated model, adapt it to existing data sources, and extend its capabilities to include very short-term (maximum 3 days) predictions for traffic and transactions. Ultimately, this project aims to create an intelligent assistant to help organize boutique operations and provide worldwide forecasts to inform strategic decision-making.

HOW WILL YOU MAKE AN IMPACT?

• Revamping the existing machine learning model within the ARIA project
• Adapting the chosen model to integrate with current and existing data sources
• Extending the models predictive scope to include traffic and transaction forecasts

REQUIRED EXPERIENCE & PROFILE:

• Engineering School or Master Degree with a specialization in applied mathematics or computer science

• Analytical mindset and structuring skills

• Fluency in English is required; French is a plus

WHAT DO WE OFFER

• We create a positive environment and highlight your personal and team performances.

• We’re fully transparent and share with you: Maison strategies, key information, needs of others Corporate Departments.

• We constantly educate you with: Cartier’s savoir-faire, competitive landscape, industry news, client experience best practices.