The Energy4Water (E4W) at UM6P is seeking a technically skilled and detail-oriented Data Scientist to lead the development of digital tools and structured data ecosystems that underpin experimental research and system design. This position focuses on scientific software development, data lifecycle management, and the implementation of streamlined, scalable workflows that support the projects innovation pipeline.
Working closely with multidisciplinary research teams, the Data Scientist will design data architectures, build modular applications for experimental tracking, and ensure long-term accessibility and traceability of datasets. The ideal candidate combines fluency in software engineering with a clear understanding of how structured data supports reproducibility, system optimization, and research integrity.
In alignment with E4Ws mission, the successful candidate will contribute to establishing a robust digital infrastructure that enhances the value, usability, and continuity of data across project activities.
Key Duties:
The successful candidate will carry out duties to the highest standard and will:
- Design and develop custom scientific software and interfaces for data acquisition, monitoring, and processing in experimental and pilot-scale systems.
- Build and maintain structured databases, metadata standards, and access protocols to ensure the organization, traceability, and scalability of research data.
- Automate data collection and cleaning pipelines, ensuring accuracy, consistency, and efficient integration across platforms and experimental stages.
- Collaborate with researchers to translate technical requirements into functional software tools and workflows that improve daily operations and research reproducibility.
- Manage version-controlled repositories for code, protocols, and datasets, in alignment with open science and institutional data governance principles.
- Support FAIR (Findable, Accessible, Interoperable, Reusable) data practices across teams and documentation processes.
- Conduct performance analysis and contribute to the optimization of experimental workflows through data-driven feedback loops.
- Participate in the development of dashboards and internal digital tools for monitoring project indicators and KPIs.
- Prepare technical reports and contribute to scientific publications where data architecture and workflow design are central to results.
- Provide technical training to project members and promote best practices in data handling, software use, and collaborative development
