Research Lead

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.