The Research Lead will provide scientific and operational leadership for AfriClimate AI’s applied research portfolio, guiding the development, fine-tuning, and validation of AI-driven climate and weather modelling tools. The role involves coordinating a multidisciplinary team, collaborating with African and international partners, and ensuring research outputs are high-quality, open, and impactful.

Research Leadership & Delivery

Lead the design, implementation, and evaluation of AI-powered, localised climate and weather forecasting methodologies.
Support the integration of observational data, satellite products, and global reanalysis datasets with AI-based and statistical models.
Develop benchmarking frameworks for model performance, including bias correction and uncertainty quantification.
Implement and manage MLOps pipelines for training, deployment, monitoring, and updating AI models in production environments.

Coordinate cross-functional teams on data collection, model training, and deployment.
Manage timelines, milestones, and deliverables in line with project objectives.

Stakeholder Engagement and Knowledge Dissemination

Collaborate with meteorological agencies, academic institutions, and industry partners.
Present research findings at workshops, conferences, and policy forums.

Publish results in peer-reviewed journals and present at leading conferences.
Contribute to open-source codebases, datasets, and technical documentation.

Essential Qualifications & Experience

PhD (or equivalent experience) in Meteorology, Climate Science, Machine Learning, or related field.
Proven track record in AI or statistical modelling for climate or weather applications.
Experience working with geospatial and gridded datasets (e.g., ERA5, CHIRPS, satellite data).
Knowledge of numerical weather prediction (NWP) systems and data assimilation.
Proficiency in Python.
Excellent communication skills required, including the ability to convey complex ideas clearly to both technical and non-technical audiences.
Strong publication record and ability to communicate research to diverse audiences.

Desirable Skills & Experience

Experience working with African climate datasets and/or in data-sparse contexts.
Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes) for model lifecycle management.
Proven experience in setting up project workflows and using tools such as Jira, with a strong understanding of agile methodologies, including Scrum.
Experience in cloud-based ML workflows (AWS, GCP, Azure) and GPU/TPU environments.
Expertise in bias correction, ensemble forecasting, and probabilistic skill assessment.
Experience in engaging with policy and decision-making communities.
Familiarity with open science and FAIR data principles.

AfriClimate AI is a grassroots research organisation advancing climate resilience in Africa through open, community-driven AI research. We focus on developing region-specific datasets, tools, and methodologies to bridge the gap between global models and local needs, supporting equitable and actionable climate solutions across the continent.

Mission-driven impact: Contribute to climate resilience and equity across Africa through open, locally grounded research.
Flexible, remote-first work: Collaborate with an international network while working from anywhere in Africa.
Leadership & visibility: Represent AfriClimate AI in high-level forums, co-author publications, and influence global conversations on AI for climate.
Open science ethos: Work in a fully open-source, community-driven environment that values transparency, reproducibility, and shared ownership.
Professional growth: Access mentorship, attend leading conferences, and shape the future of climate AI research in the Global South.
Collaborative culture: Join a multidisciplinary, values-aligned team working at the intersection of science, technology, and social impact.
Travel opportunities: Participate in key events, workshops, and field collaborations across Africa and beyond.
Competitive compensation: Receive a salary package that reflects your expertise, with flexibility for different levels of experience and location.

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