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Full-time
Data Scientist Job at Raising The Village (RTV)
Raising The Village (RTV)
Job Description
The VENN department is the data and technology backbone of our organization, connecting advanced analytics, and custom software tools with field implementation to ensure data-informed decision-making at every level.
Job Description
The Data Scientist plays a pivotal role in designing, developing, and deploying a computer vision system that transforms how RTV assesses program compliance and household adoption across last-mile communities. The role sits within the Predictive Analytics / VENN department
and is central to RTV’s image based evaluation rollout, a key pillar of the broader WorkMate AI ecosystem. The Data Scientist will work closely with, Data Scientists, ML Engineers, the Data Engineer, the Software Engineering team, and field evaluation teams to deliver an objective, scalable, and field-deployable visual assessment tool that complements and enhances RTV’s existing evaluation frameworks.
Years of Experience: 3+ years
Travel Required: Up to 30%
Duties, Roles and Responsibilities
- Research, design, and implement image classification and object detection models (including YOLO-based architectures) for automated adoption t across RTV program domains including agriculture, WASH and livestock adoption practices.
- Build and maintain end-to-end ML training, validation, and test pipelines ensuring model accuracy, reliability, and generalizability to field conditions in low-resource environments.
- Optimize models for edge deployment in environments with limited connectivity, including TensorFlow Lite integration for mobile and offline use cases.
- Design and manage image data collection protocols and annotation workflows to produce high-quality labeled datasets for compliance indicator categories across all program domains.
- Integrate image metadata and classification outputs with the RTV data warehouse (Databricks medallion architecture) for correlation with household progression and adoption metrics.
- Develop automated adoption classification outputs that map to RTV’s binary and weighted adoption scoring frameworks and validate against AHS survey-based assessments.
- Conduct structured experiments to benchmark model performance across deployment contexts (Uganda, Rwanda, DRC), applying Weights & Biases for experiment tracking and reproducibility.
- Build and document RESTful APIs to expose model predictions to WorkMate and other consuming field applications.
- Maintain clear documentation of model architectures, preprocessing pipelines, evaluation metrics, and versioning practices for cross-functional collaboration.
Qualifications, Education and Competencies
See all details of the qualifications, competencies and education for this role under the "How to Apply" section below.
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