The Researcher – Data Science will lead the design, development, and deployment of AI models for water management applications while ensuring scalability, efficiency, and seamless integration with IWMI’s data systems through end-to-end AI pipelines. The incumbent will collaborate with stakeholders and interdisciplinary teams to translate research into practical solutions and contribute to the advancement of data-driven decision-making tools for sustainable water resources.
Responsibilities:
Support the integration of AI models into IWMI’s open data platforms and digital infrastructure.
Work with remote sensing, GIS, and hydrological models to enhance AI-driven decision support systems.
Ensure FAIR data principles (Findable, Accessible, Interoperable, Reusable) are applied in all AI solutions.
Develop machine learning, forecasting, and generative AI models for hydrology and environmental applications.
Work with large-scale geospatial, hydrological, and sensor datasets to improve predictive capabilities.
Optimize cloud-based AI workflows using AWS, Azure, or GCP.
Deploy AI solutions for water data processing, risk analysis, and sustainability modeling
Implement end-to-end AI pipelines with best practices in MLOps & model versioning.
Use Docker, MLFlow, and Databricks for scalable and automated AI workflows.
Maintain model performance, accuracy, and continuous retraining for real-world applications.
Support the integration of AI models into IWMI’s open data platforms and digital infrastructure.
Work with remote sensing, GIS, and hydrological models to enhance AI-driven decision support systems.
Ensure FAIR data principles (Findable, Accessible, Interoperable, Reusable) are applied in all AI solutions.
Requirements:
PhD in AI, Data Science, Machine Learning, or closely related field, coupled with a minimum of 5 years of relevant post-doctoral experience; OR Master’s degree in the aforementioned fields, with a minimum of 10 years of relevant experience.
Strong experience in AI & ML (forecasting, Generative AI, LLMs, NLP, predictive modelling).
Experience deploying AI solutions in real-world projects & industries.
Technical leadership experience including guiding small teams in AI/ML implementation.
Proficiency in Python & AI frameworks (PyTorch, TensorFlow, OpenAI APIs, Hugging Face).
Understanding of digital twins, GIS, and environmental AI applications.
Expertise in Cloud computing (Azure ML, AWS, Databricks, Docker, MLFlow).
