Advanced Data Scientist

#WEAREALTIA

ALTIA is a multidisciplinary team focused on delivering advanced data-driven solutions that generate measurable impact and enable smarter decision-making. Working across international environments, we combine analytics, artificial intelligence, and business expertise to transform data into strategic value. Our mission is to help organizations evolve through innovation, scalable data platforms, and high-quality analytical solutions, fostering continuous learning, collaboration, and technological excellence.

We are looking for an Advanced Data Scientist & Remote Sensing AI Engineer with high-level expertise in Earth observation (EO), geospatial big data, cloud/HPC architectures, and Machine Learning / Deep Learning for wildfire monitoring and risk assessment to join our team in supporting international research and decision-support platforms (such as EFFIS and GWIS).

The role involves the spatial, temporal, and statistical integration of Earth observation data, meteorological feeds, and environmental streams with internal wildfire databases. The specialist will lead the development, calibration, and validation of automated high-resolution burnt-area mapping workflows, pan-European wildfire risk models, and wildfire behaviour/spread simulations across diverse biomes. Additionally, the candidate will contribute to big data cloud solutions, HPC data representations for AI, database optimisation, full-stack website/portal components, decision-support tools, and global fire polygon datasets.

In close collaboration with multidisciplinary teams of scientists, engineers, and policy leads, the specialist will prototype, benchmark, and validate cutting-edge geospatial algorithms and deep learning models. The position requires strong proficiency in Python, open-source GIS tools (GDAL, OSM, spatial statistics), ML/DL frameworks, and cloud-native geospatial processing environments.

WHAT WE HAVE FOR YOU?

• Remote work model (with occasional on-site presence in Ispra, Italy, if required).
• Flexible working hours.
• Long-term stable position in a highly specialised, world-class scientific environment.
• Continuous learning and upskilling in cutting-edge Earth observation, HPC/AI representations, Deep Learning, and disaster management systems.
• Integration into an experienced, collaborative, and multicultural scientific team.
• Participation in high-impact international digital and environmental intelligence projects.
• Inclusive and innovation-driven work environment.

REQUIREMENTS:

Educational level & Experience:

• EQF Level 7 (Master's degree or 5 years of higher education) + minimum 5 years of general IT experience.
• Minimum 10 years of experience as a Data Scientist working with remote-sensing and Earth observation products in the context of wildfires.
• Minimum 5 years of experience as an AI / Machine Learning Engineer in the context of wildfires.
• Minimum 5 years of experience as a Data Analyst in the same context.
• Minimum 5 years of experience as a GIS Specialist (vector data, OSM, GDAL) for wildfire monitoring.
• Minimum 2 years of experience as a Data Architect, including data representations in HPC for AI.
• Minimum 2 years of experience as a Database Engineer (analysis and performance optimisation).
• Minimum 2 years of experience as a Full-stack Software Engineer and experience as a Cloud Solutions Architect in wildfire contexts.

Languages:

• Minimum B2 level in English AND Italian (spoken and written).

TECHNICAL & FUNCTIONAL EXPERIENCE:

• Remote Sensing & Earth Observation: Medium- and high-resolution optical/radar satellite sensors, multitemporal/multisource geospatial data fusion (meteorological, environmental, EO).
• AI & Deep Learning: Traditional supervised ML, Deep Learning (neural networks, CNNs/transformers for EO), burnt-area mapping, feature extraction, model calibration, and validation.
• Wildfire Modeling & Analytics: Wildfire behaviour/spread modelling across European biomes, pan-European wildfire risk assessment frameworks, global fire polygon generation, and decision-support systems.
• Geospatial Big Data & HPC: Open GIS tools, GDAL, spatial statistics, HPC data representations for AI, cloud computing, and big data pipeline optimisation.
• Systems & Web Engineering: Full-stack development, portal editing/maintenance, database optimisation, and cloud architecture for geospatial AI workloads.

TECHNOLOGIES & SKILLS:

• Programming & Geospatial: Python, GDAL, Open Street Map (OSM), open GIS stack, spatial statistics tools.
• AI & Cloud: Machine Learning, Deep Learning frameworks, HPC representations, Big Data Cloud platforms.
• Web & Databases: Full-stack web development technologies, relational database optimisation,