Statistical Analyst for Environmental Data

h3What you will do /h3 pYou will work with a public-sector organisation on the statistical analysis of environmental datasets that have been collected over recent years but not yet systematically analysed. Your role combines hands‑on analysis, knowledge transfer, and team guidance. /p pYour work will include: /p ul liAnalysing datasets related to soil carbon monitoring, land use, surface sealing, environmental permits, and peat mapping /li liPerforming geostatistical analyses and working with geographical data /li liDeveloping and applying data mining and outlier detection techniques /li liCreating publication‑ready visualisations and reports using R Markdown /li liSetting up and executing quality control processes for published data and documents /li liExploring Natural Language Processing (NLP) techniques for automated processing of permit decisions from unstructured PDF documents, including extraction, categorisation and interpretation of conditions and obligations /li liConducting individual analyses and guiding a team of internal staff members /li liDelivering training sessions and collaborative work sessions to build internal capacity for statistical analysis /li liExplaining your analyses clearly and repeating them together with colleagues so that similar analyses can be performed autonomously in the future /li /ul pThe organisation wants to raise the level of statistical literacy and move towards publication‑ready statistical work, with more attention to visualisation and communication of results. /p h3What we are looking for /h3 pbEssential: /b /p ul liMaster's or bachelor's degree with a technical or engineering background, or equivalent experience with the relevant ICT technology /li liAt least 10 years of experience with R /li liAt least 10 years of experience with statistical software packages /li liAt least 10 years of experience with the technology in question or similar technology /liliAt least 3 years of experience delivering training /li liExperience in the public sector /li liDutch at CEFR level C2 /li /ul pbHighly valued: /b /p ul liFurther experience within the public sector /li liAt least 10 years of experience with data mining and outlier detection /li liExperience with data visualisation in R Markdown /li liExperience with GIS analyses and geographical data /li liExperience with open source development /li liAt least 5 years of experience with Python /li /ul pbUseful: /b /p ul liExperience with neural networks /li /ul pYou are strongly communication
- and analysis-oriented, with in-depth knowledge of statistical methods and their application. /p h3The setting /h3 pThis assignment runs until mid-July 2027, with a workload of around 50 per cent (approximately 768 hours in total). /p pYou will be on‑site in Brussels one or two days per week, with the remainder of your time remote or available via Teams. A maximum of 50 per cent of the work can be done remotely. /p pThe internal team members you will support come from diverse backgrounds, are all university‑educated, and are interested in statistical analysis. /p pWork is in Dutch. /p pYou can join us for this assignment as a freelancer or as an employee of HumanInTech. Same role, same team. If you join as an employee, your employment continues beyond this assignment. When it ends, we’ll work together to find your next assignment. /p pbLocation: /b Brussels, hybrid /p pbWork address: /b Belgium /p pbEmployer / contracting party: /b HumanInTech /p pbApplications close (Brussels time): /b November 10, 2026 at 1:00 AM /p pbEngagement: /b Freelance or employed by HumanInTech /p pbWorking hours: /b Full‑time /p pbExperience: /b More than 10 years or more /p pbEducation: /b Bachelor's or more /p pbPublished: /b November 2026 /p