Advanced analytics on territorial data: key competencies for the specialist profile
The integration of artificial intelligence models in the analysis of territorial data requires a technical profile that combines mathematical-statistical expertise with operational capability in heterogeneous data environments. Work on Earth observation imagery, geospatial time series or data streams from distributed sensors demands not only the design of machine learning architectures, but also an understanding of acquisition conditions, instrumental biases and coverage limitations that affect the quality of the input data. Without that domain knowledge, the resulting models lack the robustness required for use in institutional decision-making contexts. Predictive and prescriptive analytics applied to critical infrastructure, territorial management or environmental monitoring introduces additional requirements compared to academic research environments. Models must operate on data from multiple domains — optical, radar, GNSS, meteorological, socioeconomic — whose correlation is neither trivial nor stable over time. The expert in this field must manage data pipelines with irregular ingestion, coordinate the fusion of sources with disparate spatial and temporal resolutions, and maintain the traceability of the inference process throughout the entire model lifecycle. One of the most demanding aspects in governmental and infrastructure-operation contexts is the validation and explainability of results. Public administrations and institutional operators cannot accept model outputs as black boxes: they require technical justification of predictions, quantified uncertainty metrics and reproducible audit procedures. This means that the professional profile must incorporate competencies in model interpretability techniques, cross-validation experiment design and technical documentation oriented towards third-party review, including regulatory bodies or auditors of publicly funded projects. Interdisciplinary collaboration is a structural condition of this type of work. The AI and advanced analytics specialist operates alongside observation system engineers, GNSS specialists, sectoral domain analysts and data platform architects. This interface position demands the ability to translate operational requirements into model specifications, and to communicate technical limitations to interlocutors who are not specialised in machine learning. Experience in multi-domain or multi-sensor projects is, in this respect, a relevant indicator of professional maturity. NASSAT has an open vacancy for an AI and Advanced Analytics Expert, in a full-time remote working arrangement. The required profile includes a Bachelor's or Master's degree in Engineering, Mathematics, Physics, Statistics, Data Science or an equivalent discipline, together with a minimum of three years of accredited experience in AI models, machine learning, predictive or prescriptive analytics, or multi-domain correlation systems. Full information on the position and the application process is available on the NASSAT Employment Portal at nassat.com/empleo.html.
NASSAT - Network Satellite Systems