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CITEVE in Vila Nova de Famalicão, Portugal, seeks a seasoned Data Science Team Lead to drive applied research at the Digital Transition Department. You will lead the data science group, shape project agendas, and deliver intelligent manufacturing and supply-chain solutions with industry partners.
Responsibilities include guiding model development from PoC to production, collaborating across teams, and mentoring researchers while aligning with strategic research goals.
Data plays a central role in the transformation of industry, and the textile and clothing sector is no exception. That is why we, at CITEVE, see this as both a challenge and an opportunity, whether it is data related to engineering and product development, manufacturing processes, logistics and distribution or information services, so we step forward. We built a team of applied researchers to develop innovative solutions for the textile and clothing industry, and now we need a team leader.
The candidate will join the Digital Transition Department and will have the responsibility to lead the data science group and its projects and initiatives and to successfully develop innovative and intelligent solutions for manufacturing and management processes, product engineering, and supply-chain challenges in the textile and clothing industry.
The Research Team Lead reports directly to the Director of the Digital Transition Department. The role is a collaborative one: most of our projects are delivered jointly, so the team lead is expected to work closely with the other teams within the department and with teams across CITEVE, articulating the data science contribution with their areas of expertise and with the needs of the companies we serve.
The position is for full-time work at our facilities in Vila Nova de Famalicão, Portugal, with the possibility of a hybrid-remote regime.
Our data science work is applied research carried out inside real factories, alongside textile and clothing companies, technology providers and other R&D centres, mostly within national and European collaborative programmes. In practice, that means building predictive models of manufacturing processes from imperfect shop-floor and sensor data; creating datasets where none exist, through capture and annotation pipelines, simulation and synthetic data; putting large language models to work as an interface to industrial data, such as retrieval-augmented generation and conversational agents that turn a question asked in plain Portuguese into a query over MES, ERP and sensor databases, so that domain experts reach their own data without writing SQL; and coupling forecasts with optimisation, so that a prediction becomes an actionable plan. We take solutions from proof of concept through to validated operation on a production line, which demands sound data engineering, disciplined validation, and careful handling of proprietary data belonging to partners who often compete with one another.
CITEVE will only respond to selected applications for interviews