Richemont Industrial Data Science Intern

Richemont is a Swiss-based luxury goods group composed of distinguished maisons across watches, jewellery and accessories. The group combines traditional craftsmanship with digital and industrial innovation, investing in R&I initiatives that modernize manufacturing, quality and customer experience while preserving artisanal excellence.

1. Ingest, clean and harmonize industrial and sensor datasets from manufacturing lines, assembly benches and quality control systems.
2. Perform exploratory data analysis and feature engineering to identify patterns relevant to process stability, quality and asset health.
3. Develop, validate and tune predictive models (e.g., time-series forecasting, anomaly detection, predictive maintenance) to reduce downtime and improve yield.
4. Prototype analytics pipelines and reproducible notebooks; collaborate with data engineers to prepare production-ready data flows.
5. Work closely with R&I engineers, production teams and quality managers to translate model outputs into actionable recommendations and KPIs.
6. Document methodologies, produce technical reports and present findings to multidisciplinary stakeholders.

1. Currently enrolled in a Master's or engineering programme in Data Science, Statistics, Computer Science, Industrial Engineering or a closely related field.
2. Strong programming proficiency in Python and experience with data science libraries (e.g., pandas, scikit-learn).
3. Practical knowledge of machine learning for time-series, anomaly detection or predictive maintenance.
4. Experience with SQL and working with relational or time-series databases.
5. Ability to communicate technical results to non-technical stakeholders and work in cross-functional teams.

1. Python
2. pandas
3. scikit-learn
4. SQL
5. Time-series analysis
6. Machine learning
7. Data visualization (e.g., matplotlib, seaborn, Plotly)
8. Jupyter notebooks
9. Model validation and evaluation metrics
10. Technical communication and stakeholder engagement

Practical project experience applying supervised and/or unsupervised learning to real datasets, ideally including time-series or sensor data from industrial or manufacturing contexts. Prior internship or academic research involving end-to-end model development and clear documentation is strongly preferred.

Enrolled in a relevant Master's or engineering programme (Data Science, Computer Science, Statistics, Industrial Engineering or equivalent).

Richemont blends heritage craftsmanship with a forward-looking R&I culture that values technical excellence, cross-disciplinary collaboration and respect for artisanal processes. Teams operate in a matrixed environment connecting research, production and maison stakeholders to deliver scalable, high-quality innovation.