[Hiring] Senior Geospatial Data Scientist @Meridia Land

Role 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
• Traceability

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

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

Requirements

• 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.

Benefits

• Initial 3-month engagement, with possible extension depending on progress and mutual fit.
• 2-3 days per week.
• Fully remote.
• Software subscriptions provided by Meridia where applicable.
• Monthly invoicing against agreed milestones and deliverables.
• Work on a data lake covering 20%+ of the world’s agricultural land, alongside a team of 70+ agriculture, data and tech experts.
• Direct impact on EUDR, DCF and Scope 3 compliance work.

How to apply

If this opportunity matches your experience, please apply on Workable: https://apply.workable.com/j/739CC757DB

Your application should include:

• CV highlighting relevant experience in geospatial data science and machine learning for agricultural applications.
• A brief proposal (max 3 pages) outlining your approach.
• Examples of previous work.
• Your day rate and availability.