Environmental Data Scientist
Pubblicato il 15-08-2026 - Uni Systems in Varese
At Uni Systems, we are working towards turning digital visions into reality. We are continuously growing and we are looking for a professional Environmental Data Scientist to join our UniQue Ispra team.
The successful candidate will provide technical support to a scientific project involving the management, processing and analysis of large and heterogeneous environmental datasets. The role will focus on collecting and organizing data from existing sources, developing tools for data processing and quality control, and supporting the analysis of Earth Observation data and model outputs. The candidate will work with datasets from different sources and formats and contribute to the development of reliable and consistent data processing workflows. The position requires strong programming and data analysis capabilities, particularly in Python and R, together with experience in Linux/Unix environments and the management of scientific and geospatial data.
What will you be bringing to the team?
- Gathering relevant data from existing databases and data sources;
- Developing tools to read data in different formats, detect errors and organize them into consistent datasets;
- Developing tools for data analysis, harmonization, quality control and visualization;
- Supporting the processing and analysis of Earth Observation data;
- Supporting the assessment of Earth Observation data and model outputs using reference datasets;
- Supporting the preparation and implementation of data processing and analysis workflows;
- Managing and analyzing large datasets from different sources and formats.
What do you need to succeed in this position?
- Master Degree + 13 years of relevant professional experience
At least 3 years of specific expertise in:
- Python programming
- R programming language
- Unix environment
- Management of data in various formats (e.g., CSV, NetCDF, Shapefile …)
- Field of data management and analysis
- At least 2 years of specific expertise in statistical analysis o