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.

Key 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.

Technical Requirements

● Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics (Statistical

computing )or a related quantitative field.

● 3+ years of hands-on experience in machine learning and computer vision, with a

demonstrable portfolio of deployed models.

● Proficiency in:

○ Python (PyTorch or TensorFlow) for deep learning model development.

○ Object detection and image classification frameworks, particularly YOLO

architectures (YOLOv8 or later).

○ Data annotation tools and active learning workflows for building labeled datasets.

○ Cloud platforms, specifically AWS, for model training, storage, and deployment.

○ SQL and familiarity with data warehouse environments (Databricks preferred) for

integrating model outputs with structured household data.

○ Model deployment and MLOps practices, including CI/CD pipelines and

experiment tracking with Weights & Biases or equivalent.

○ Edge deployment optimization (TensorFlow Lite, ONNX) for low-connectivity field

environments.

● Experience building and documenting RESTful APIs to expose model predictions to

consuming applications.

● Familiarity with mobile data collection platforms (SurveyCTO, ArcGIS, Custom APPs)

and field data workflows in development or humanitarian contexts is an asset.

Personal Attributes

● Deep commitment to applying data science for social impact and poverty alleviation.

● Strong analytical and problem-solving mindset with attention to field-level constraints and

practical deployment realities.

● Ability to communicate complex model outputs to non-technical stakeholders including

field officers and program managers.

● Collaborative team player who thrives in a fast-paced, mission-driven environment with

multiple concurrent workstreams.

● High degree of independence, initiative, and commitment to integrity and innovation.

Raising The Village is committed to Equity and Inclusion in the workplace and is proud to be an

equal opportunity employer.