Powering data-driven sustainability assessment tasks in Agri-food systems with IoT-data Data-lakes and large language models

This mid-level role requires expertise in data-driven sustainability assessment within agri-food systems, emphasizing the integration of IoT data, data lakes, and large language models. Candidates should have a strong foundation in managing and analyzing large-scale IoT datasets collected from agricultural and food production environments. Proficiency in designing, implementing, and maintaining data lakes is essential to efficiently store and process diverse and voluminous data sources. Experience with large language models (LLMs) is critical for extracting insights, automating analysis, and supporting decision-making processes related to sustainability metrics. A master's degree indicates an advanced understanding of relevant fields such as data science, environmental science, agricultural technology, or computer science. The role demands skills in data engineering, machine learning, and natural language processing, alongside knowledge of sustainability assessment frameworks and agri-food system dynamics. Familiarity with cloud platforms and big data tools for scalable data processing is likely important. The candidate should demonstrate the ability to collaborate across interdisciplinary teams, translating complex data outputs into actionable sustainability strategies. Overall, this position blends advanced data science techniques with domain-specific knowledge in agriculture and sustainability, aiming to leverage cutting-edge AI and IoT technologies to drive informed, data-backed sustainability assessments in agri-food systems.