Sephora Data Science Intern
- Location
- ParisÎle-de-FranceFrance
- Employment
- Internship
- Seniority
- Intern
- Posted
- Jun 30, 2026
About Sephora
Sephora is a global prestige beauty leader, renowned for redefining the retail experience through creativity, inclusivity, and expert service. Founded in France and now part of LVMH, the company brings together an exceptional portfolio of beauty brands with a distinctive culture of discovery and innovation. As an employer, Sephora offers a dynamic, customer-centered environment where talent is encouraged to learn, experiment, and grow across retail, digital, merchandising, operations, and corporate functions. Its teams are united by a passion for beauty, entrepreneurship, and belonging, making Sephora a compelling workplace for professionals seeking impact within a fast-moving international brand.
Sephora Data Science Intern (6 months, September 2026) in Paris La Défense. ML algorithm development with Dataiku DSS and GCP.
Role & Responsibilities
- Develop and optimize machine learning algorithms (RandomForest, XGBoost, generative AI, etc.), including feature engineering and hyperparameter tuning
- Master Sephora's data ecosystem (Dataiku DSS, Google Cloud Platform) and implement algorithms across priority axes: product recommendation, customer scoring, activity forecasting, and generative AI
- Conduct model monitoring and A/B testing to facilitate adoption by business teams
- Identify optimization opportunities and contribute to projects leveraging large volumes of transactional, online, product, and inventory data
- Collaborate with cross-functional data science teams to deliver concrete, measurable business impact
Qualifications
- Bachelor's degree +5 years or equivalent in Data Science, Machine Learning, or related quantitative discipline
- Mastery of primary machine learning approaches: RandomForest, XGBoost, LightGBM, LSTM
- Proficiency in SQL and Python
- Demonstrated experience through machine learning projects or Kaggle competition participation
- Knowledge of Dataiku DSS and Google Cloud Platform ecosystems (preferred)
- Strong organizational and dynamic execution capability with proactive contribution mindset
- Continuous engagement with emerging machine learning methodologies and best practices
Skills
Experience
Beginner-level professionals with demonstrated academic or portfolio-based machine learning project experience are encouraged to apply. Kaggle competition participation or comparable practical data science engagement is valued.
Education
Bachelor's degree (+5 years of study) with specialization in Data Science, Machine Learning, Statistics, or closely related quantitative field.
Workplace
This position is based in Paris, Île-de-France, France.
Culture
Sephora fosters a dynamic, inclusive workplace where creativity, curiosity, and a passion for beauty are encouraged at every level. As an employer, it is distinguished by its focus on empowerment, continuous learning, and a collaborative retail culture that celebrates individuality and client-centric innovation.
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 Sephora, 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 «Machine Learning Intern», «Data Scientist Trainee», «Assistant Data Analyst», «Data Science Apprentice», and other variations. Our sophisticated search architecture anticipates these variations, ensuring that inquiries using related terms will seamlessly yield the exact roles you desire.