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.

**Requirements**:
**Essential Qualifications & Experience**
- PhD (or equivalent experience) in Meteorology, Climate Science, Machine Learning, or related field.
- 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.

**Benefits**

**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.