AI-Ready Data Engineering for Scientific Data

SFBI; Marquette-lez-Lille; Vor Ort; EUR 6.700 - 11.000; Vollzeit

Benefits dieser Stelle

Open to people with disabilities

Zusammenfassung

Lesaffre is offering an internship at the NMH/BioData interface to build a web interface (Streamlit) and a Python layer that converts raw instrument output into a clean, describable, queryable experimental result. You will work with NMH scientists and a BioData DevOps engineer to enable AI-ready data, reproducibility, and data-driven insights.

The role focuses on automated data ingestion, schema modeling, and developing queryable data pipelines, with emphasis on clean code, testing, and

Qualifikationen

• Master 2 or final year of Engineering School at a French university or higher education institution (mandatory).
• Must hold valid authorization to work in France.
• Major in Computer Science, Software Engineering or Data Engineering.

Aufgaben

• Instrument data acquisition & parsing from laboratory instruments and formats.
• Data standardization & schema modeling for interoperability and reproducibility.
• Architect datasets and develop tools/endpoints for autonomous querying and analysis.
• Expose processing services via REST APIs and lightweight interfaces like Streamlit.
• Follow Biodata's software engineering practices: Docker, Git, tests, and documentation.

Kenntnisse

Python

Git

Docker

Data processing

Ausbildung

Master 2 / Engineering School (final year)

Authorization to work in France

CS/Software/Data Engineering focus

Tools

Streamlit

REST APIs

JSON Schemas

Jobbeschreibung

Lesaffre is offering an internship at the NMH/BioData interface to build a web interface (Streamlit) and a Python layer that converts raw instrument output into a clean, describable, queryable experimental result. You will work with NMH scientists and a BioData DevOps engineer to enable AI-ready data, reproducibility, and data-driven insights.

The role focuses on automated data ingestion, schema modeling, and developing queryable data pipelines, with emphasis on clean code, testing, and