คำอธิบาย
Expected missions / assignments
The student will contribute to the full data valorization lifecycle in an R&D context:
- Analyze and understand raw experimental and embedded-system data from chassis-related tests
- Relate datasets to their physical, experimental, and engineering context
- Define and implement data quality, consistency, and traceability rules
- Structure and prepare datasets suitable for:
- Engineering and business analysis
- Machine Learning model development
- future Generative AI and engineering assistant use cases
- Develop reproducible and automated data pipelines using Python and SQL
- Implement dataset versioning and comparison across test campaigns
- Document data transformations to ensure transparency and explainability
- Explore datasets to identify value-added information:
- Trends and patterns
- Variability and correlations
- Atypical or unexpected behaviors
- Contribute to analyses with tangible impact for R&D teams
