Overview
Experienced Data Scientist to design and deploy machine learning and deep learning solutions for early warning systems and humanitarian decision-making within the CLEAR infrastructure.
Key Responsibilities
- Support the design and implementation of CLEAR’s data architecture.
- Partner with developers and data engineers to design and stand up the environment needed to train and fine-tune models.
- Develop machine learning and deep learning models that integrate multiple data streams to detect early indicators of humanitarian crises.
- Build computer-vision and remote-sensing models on satellite and aerial imagery.
- Contribute to building automated alert systems that identify emerging crises.
- Create ensemble modelling approaches that combine traditional statistical methods with advanced AI techniques.
- Fine-tune and adapt foundation models and large language models to humanitarian use cases.
- Explore venues for adapting models to evolving crisis conditions through reinforcement learning systems.
- Implement impact-based forecasting systems that translate predictions into specific humanitarian consequences.
- Build decision trees and recommendation engines that guide field staff through systematic needs assessment processes.
- Create automated reporting systems and interactive dashboards.
- Any other tasks assigned by the CLEAR Technical Lead and AI Lead related to CLEAR data.
Required Experience
- Minimum of 5 years of professional experience in applied data science.
- Demonstrated experience designing, training and fine-tuning deep learning models.
- Advanced proficiency in Python for statistical analysis, machine learning and data manipulation.
- Strong SQL skills for database management and complex query optimization.
- Hands-on experience implementing supervised and unsupervised learning algorithms.
- A proven track record of proactively sourcing and engineering data.
- Experience designing and implementing ETL pipelines for processing datasets from multiple sources.
- Ability to create automated reporting systems and dashboards.
- Experience working with large, messy, real-world datasets.
- Understanding of model deployment and MLOps practices.
- Fundamental skills with version control software and collaborative development.
- Practical experience working in low-data or data-scarce settings.
- Experience implementing and fine-tuning large language models (LLMs) for applied tasks.
- Experience with natural language processing techniques.
- Experience using GenAI for automated analysis of large volumes of documents.
- Understanding of ensemble methods and explainable AI techniques.
- Experience working with earth observation and satellite imagery.
- Applying computer vision and deep learning to imagery for humanitarian tasks.
- Experience with multimodal data fusion.
Qualifications
Advanced degree in Data Science, Statistics, Computer Science, Physics, Engineering, Economics or a related quantitative field.
