University Rennes 2 is the host institution of the research project "STAGE: From Stage to Data, the Digital Turn of Contemporary Performing Arts Historiography", which was awarded an ERC advanced grant (2,5 million €). At the intersection of history, epistemology, and digital humanities, STAGE’s key goal is to move performing arts studies into a digital context to establish a new historiography of mise en scène and creative processes in Europe since WWII. The team is composed of up to 10 people (researchers, data scientists, data engineer, junior developer, PhD students).

As Data Scientist / MLOps engineer, you will play a leading role in an international research project, pro-actively contributing to the design and development of datafication processes sourced from multimodal documents. You will be expected to conduct high-quality research, coming up with creative solutions, leading initiatives end-to-end, interacting with peers and researchers worldwide. You will also help promote machine learning best practices and encourage new approaches to problem-solving within the team. Responsibilities include software development, machine learning, as well the identification and implementation of data mining tools that will be tailored to the specific requirements of the project to establish processing pipelines.

If you are passionate about driving innovation and reshaping historiography in the digital age, we invite you to apply to join an open team with a modern working culture.

Key Responsabilities

• Design and maintain scalable ETL and MLOps infrastructure for datafication processes from historical and performing arts company sources (mainly playbills, texts, photographs and videos) including time-series data and network analysis
• Perform state-of-the-art research into machine learning approaches, methodologies and tools. Develop POC to draw insights from unstructured multimodal data in close collaboration with domain experts involved in the project (senior researchers, PostDocs, PhD students)
• Work with data engineers and researchers in arts & humanities to ensure optimal data acquisition, data integrity and data pipelines
• Supervise a junior developer and mentor team members, sharing your expertise and best practices in ML engineering, MLOps, and software development to cultivate an environment of experimentation and learning
• Write research papers detailing new findings, techniques, and improvements. Present research findings at conferences, workshops, and seminars
• Stay up-to-date with the latest advancements in machine learning, AI technologies, and incorporate them into our solutions