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# Senior Geospatial Data Scientist

- **Company:** Meridia Land
- **Location:** Netherlands (Remote available)
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Geographic Information Systems, Network Analysis, Computer Programming, Spatial Databases, R (Programming Language), Python (Programming Language), Machine Learning, PostGIS, Git, Containerization, Information Technology, Geospatial Data Abstraction Library (GDAL), Docker
- **Published:** October 16, 2026
- **Apply:** https://www.adzuna.nl/details/5929123317

## About the Role

* Advanced degree (MSc or PhD) in Remote Sensing, Geospatial Science, Computer Science, or related field.
 * 10+ years of practical experience in applying statistical and earth observation techniques to agricultural applications.
 * Proficiency in programming languages commonly used in geospatial analysis and machine learning (Python, R, Google Earth Engine).
 * Knowledge of agricultural systems and supply chains in tropical and subtropical regions.
 * Experience with modelling approaches relevant to supply chain analysis, such as optimisation, spatial allocation, entity matching or network analysis across heterogeneous datasets.
 * Strong geospatial data engineering skills: spatial databases (e.g. PostGIS), GDAL/geopandas or equivalent, and handling of large raster and vector datasets.
 * Experience building reproducible, version-controlled analytical pipelines (Git; containerisation such as Docker is a plus).
 * Proven ability to document methodologies rigorously, including data provenance, assumptions, limitations and licensing.
 * Ability to work independently in a remote setting, with strong communication skills and full professional proficiency in English.

Nice to have

 * Familiarity with agri-commodity sectors (e.g. palm oil, soy, rubber, cocoa, coffee) and their data landscape, including production statistics, mill or processor lists, and sourcing structures with producers and intermediaries.
 * Familiarity with (agricultural) supply chain transparency and traceability approaches, logistics, procurement and certification.
 * Working knowledge of relevant frameworks (EUDR, DCF/NDPE, certification schemes) and how production area and sourcing area information supports risk assessment under them.
 * Experience validating models against ground-truth or in-situ field data, and quantifying and communicating uncertainty.
 * Experience working with production statistics and yield data in data-scarce environments.
 * Track record of peer-reviewed publications or public technical documentation in a related domain.
 * Prior experience in Southeast Asia and/or Latin America.

## Description

This document outlines the scope and objectives for hiring a consultant to support Meridia's first-mile intelligence work: the data science that connects commodity supply chains to the production areas they source from. This includes production area assessment, supply shed modelling and traceability, drawing on a variety of data sources and combining geospatial, statistical and ML approaches.

Understanding where commodities come from, without needing every farm boundary, helps buyers make credible, evidence-based sourcing risk assessments, while keeping smallholders included in supply chains. It supports deforestation- and conversion-free (DCF), EUDR and Scope 3-related risk assessment.

Objectives

 * Develop accurate, high-quality models and datasets for production area assessment and sourcing area (supply shed) analysis across multiple commodities and origin countries.
 * Leverage Meridia's in-situ farm plot datasets to train and validate models.
 * Adapt existing machine learning models to local agricultural contexts to improve prediction accuracy.
 * Create scalable and reproducible methodologies that work across different geographies and commodities.

Deliverables

 1. Methodology and codebase
 2. Organised folder with all source files
 3. Full documentation including operating workflow, methodology, sources (including retrieval and licensing details)