• 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 (including data ingestion pipelines, compute and GPU resources, experiment tracking and MLOps tooling) actively shaping that environment rather than waiting for it to be provided. • Develop machine learning and deep learning models that integrate multiple data streams to detect early indicators of humanitarian crises, combining earth observation and satellite imagery, conflict event data, climate monitoring, economic indicators, and population movement patterns into unified risk assessment frameworks. • Build computer-vision and remote-sensing models on satellite and aerial imagery (for example flood-extent mapping, building and settlement detection, displacement-site monitoring, infrastructure damage assessment and land-cover change detection) and fuse these earth observation outputs with non-imagery signals. • Contribute to building automated alert systems that identify emerging crises, calibrating prediction algorithms for different crisis types. • 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 such as document triage, multilingual report analysis and situation summarisation. • Explore venues for adapting models to evolving crisis conditions through reinforcement learning systems. • Implement impact-based forecasting systems that translate meteorological, conflict, and economic predictions into specific humanitarian consequences such as displacement volumes, food insecurity levels, and infrastructure damage estimates. • Build decision trees and recommendation engines that guide field staff through systematic needs assessment processes informed by predictive analytics and historical response data. • Create automated reporting systems and interactive dashboards that enable field teams and leadership to access real-time data for rapid response activities.

• Design, develop, and maintain machine learning models • Build and deploy ML pipelines on AWS • Develop advanced NLP solutions • Design and productionize RAG systems • Develop LLM-powered applications • Implement predictive analytics and statistical modeling • Perform data mining and exploratory data analysis • Build data visualizations and dashboards • Evaluate model performance and present results • Collaborate in an Agile environment

• Providing advanced analytics, ML model design, technical leadership. • Collecting, analyzing and interpreting large data sets to identify trends, patterns, and provide key business insights. • Performs data mining, cleaning, and aggregation processes to prepare data, implement data models, conduct analysis, and develop databases. • Develops insights and reports from multiple structured and unstructured data sources using programming, statistical, and analytical techniques and tools. • Maintains continuous collaboration with teams to understand the underlying purpose, focus, and objective of each data analysis project to ensure alignment and support. • Designs, develops, and implements the most valuable data-driven solutions for the organization. • Works autonomously, but may provide a leadership role for the work group through knowledge in the area of specialization.

• Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. • Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). • Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. • Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. • Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). • Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. • Monitor model performance, conduct error analysis, and continuously optimize pipelines.