Research Team Lead / Data Science (M/F)

Summary

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.

The work: how we approach data science at CITEVE

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.

Qualifications and experience

• MsC or PhD degree in computer science, informatics, data science, or similar.
• 4+ years of experience in data science, AI, and machine learning projects and/or initiatives, ideally in an industry-related context (send us your project portfolio);
• Robust knowledge of statistical concepts, accompanied by expertise with a set of analytical tools ranging from databases (e.g. SQL, MongoDB) to programming languages (e.g. Python, Golang). Understanding data engineering solutions is a plus.
• Practical command of both classical machine learning (regression, classification, tree-based ensembles and gradient boosting) and deep learning frameworks (e.g. PyTorch, TensorFlow), with the judgement to choose between them on the evidence.
• Experience carrying models beyond proof of concept into operational use (validation protocols, model versioning and experiment tracking, containerised deployment and inference services).
• Experience in applied R&D projects and activities.
• Continuous learning mindset, staying updated with the latest technologies and industry trends.
• Knowledge of version control tools (git) and containers (docker).
• Strategic thinker able to contribute to research agendas, in collaboration with industry.
• Ability to build and discuss new project requirements definition alongside the project manager and stakeholders.
• Team management skills: Ability to effectively lead, guide, and oversee the Data Science team to achieve their goals and work together cohesively.
• Ability to collaborate with the other teams of the Digital Transition Department and of CITEVE, integrating the team's work into wider, multidisciplinary projects.
• Strong organisational skills, with emphasis on priorities and goal setting;
• Presentation and communication skills, both written and verbal (EN and PT);
• Experience in project management.
• Must be comfortable with both in-person and video conferences.
• Ability to work independently and be self-motivated, as well as to collaborate in a team environment.

Valued, though not essential

• Experience with computer vision in industrial or otherwise uncontrolled environments, including dataset construction and annotation strategy.
• Familiarity with large language models applied to enterprise data, retrieval-augmented generation, natural-language querying of structured databases, and the evaluation of such systems.
• Experience with time-series and IoT data pipelines, streaming protocols and digital twin or semantic data architectures.
• Exposure to optimisation and scheduling methods alongside predictive modelling.
• Use of simulation or synthetic data generation to overcome limited training data.
• Experience in publicly funded collaborative projects (e.g. RRP mobilising agendas, Horizon Europe), including proposal writing, deliverables and dissemination.
• Prior work with the textile and clothing sector is welcome but not required, we will teach you the domain.

Benefits

• Remuneration based on your experience and skills.
• Health insurance plan.
• Being part of a research centre with a renowned history in innovation and technology, and opening up new fields of research.
• Play a leading role in the process of innovation and creativity of new approaches and solutions for the sector.
• A portfolio of concurrent applied projects across manufacturing, product development and supply chain, giving unusually broad exposure to real industrial data.
• Development and training opportunities.
• Collaboration with European partners.
• Possibility of hybrid remote working.

Applicants should send a detailed CV and a list of projects or initiatives carried out and relevant to this position, with the reference RH09/2026, by 18/09/2026, through the application form available on the CITEVE website under Careers.

CITEVE will only respond to selected applications for interviews