Spatial Data Scientist – Machine Learning & Remote Sensing -(Globally Recruited Staff (GRS) position)

CIFOR-ICRAF is looking for a Spatial Data Scientist – Machine Learning & Remote Sensing

Locations: Global

Organization: The Center for International Forestry Research (CIFOR) and World Agroforestry (ICRAF)

Deadline: 03 April 2026

Job Description

CIFOR-ICRAF

The Center for International Forestry Research (CIFOR) and World Agroforestry (ICRAF) envision a more equitable world where trees in all landscapes, from drylands to the humid tropics, enhance the environment and well-being for all. CIFOR and ICRAF are non-profit science institutions that build and apply evidence to today’s most pressing challenges, including energy insecurity and the climate and biodiversity crises. Over a combined total of 65 years, we have built vast knowledge on forests and trees outside of forests in agricultural landscapes (agroforestry). Using a multidisciplinary approach, we seek to improve lives and to protect and restore ecosystems. Our work focuses on innovative research, partnering for impact, and engaging with stakeholders on policies and practices to benefit people and the planet. Founded in 1993 and 1978, CIFOR and ICRAF are members of CGIAR, a global research partnership for a food secure future dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resources.

Overview

Under the supervision of the head of SPACIAL the Spatial Data Scientist – Remote Sensing will lead and implement advanced remote sensing analysis tasks, including processing of optical and SAR data, timeseries analysis, and predictive modeling with primary focus on the modeling of temporal dynamics across complex landscapes. The position will support a range of projects and programmes across CIFOR-ICRAF, including Regreening Africa, Knowledge for Great Green Wall Action (K4GGWA). Towards Ending Drought Emergencies (Twende), and upcoming assessments of soil and land health with funding from NORAD.

Duties and responsibilities

Spatial data science

Design and implement machine learning pipelines for geospatial analysis, including feature engineering, model selection, hyper parameter tuning, and validation.
Develop and deploy deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting.
Apply advanced AI techniques for predictive modelling and mapping of indicators relevant to ecosystem health assessment using field data and multi-source remote sensing.
Process and analyze optical data (Sentinel 2, Landsat 8/9) and SAR data (Sentinel 1), including data fusion and feature extraction for ML workflows.
Implement time series analysis and forecasting models, including trend detection, anomaly identification, and predictive analytics for vegetation, precipitation, and land surface dynamics.
Develop scalable, reproducible spatial data processing workflows and contribute to MLOps practices.
Supervise a team of junior spatial data scientists and developers.
• Develop communication products/outputs where relevant.

Capacity development

Lead internal capacity development seminars within CIFOR-ICRAF on machine learning, AI applications, and spatial data science.
Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on ML-driven spatial analysis and modelling.

Stakeholder engagement

Work closely with the CIFOR-ICRAF stakeholder engagement team (SHARED) to provide AI-driven analytical outputs that feed into project delivery, for example monitoring outputs as part of the Great Green Wall.
Contribute to stakeholder engagement events as part of the development of decision support tools and platforms.

Various other tasks

Contribute to micro-dashboard development as part of the Global Resilience Impact Tracker platform
Support projects and programs with analytical support and stakeholder engagement with decision makers.
Lead and/or contribute to scientific papers.
Contribute to proposal development and writing.

Requirements

PhD or MSc degree in spatial data science, geoinformatics, computer science, or a related quantitative field with demonstrated expertise in machine learning and AI applications.
Proven experience developing and deploying machine learning models for geospatial applications.
Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with architectures such as CNNs, RNNs, LSTMs, and Transformers.
Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for geospatial analysis.
Knowledge of remote sensing data processing and analysis, including optical and SAR platforms.
Excellent interpersonal skills.
Excellent written and spoken English. Knowledge of French a plus.

Education, knowledge and experience

• PhD or MSc degree in spatial data science, geoinformatics, computer science, or a related
quantitative field with demonstrated expertise in machine learning and AI applications.
• Proven experience developing and deploying machine learning models for geospatial applications.
• Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with
architectures such as CNNs, RNNs, LSTMs, and Transformers.
• Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
• Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for
geospatial analysis.
• Knowledge of remote sensing data processing and analysis, including optical and SAR platforms

Terms and conditions

• This is a Globally Recruited Staff (GRS) position. CIFOR-ICRAF offers competitive remuneration in USD, commensurate with skills and experience.
• The appointment will be for two (2) year period, inclusive of a six-month probationary period, with the possibility of extension contingent upon performance, continued relevance of the position and available resources.
• The duty station will be in Kenya, Nairobi or Remote.