Data Scientist Job
Work Hours: Full-time, 08 hours per day
Salary:
Attractive
Job Deadline: 31 March 2026
Number of Jobs: 01
Hiring Entity: RAISING THE VILLAGE
 |
| RAISING THE VILLAGE |
Location: In Uganda
Job Details:
Job Title: Data Scientist Department/Group: VENN
Reporting To: Senior Data Scientist Years of Experience: 3+ years
Location: Mbarara Travel Required: Up to 30%
About Raising The Village
At Raising The Village (RTV), we are dedicated to eradicating ultra-poverty in Sub-Saharan
Africa. As a dynamic, rapidly growing international development organization, we’ve assembled
a team of over 250 passionate individuals in Uganda, alongside an additional 17 professionals
in North America and 15 in Rwanda. Together, we are committed to elevating communities out
of ultra-poverty by implementing innovative solutions and leveraging advanced data analytics to
drive impact.To date, our holistic approach has positively impacted over 1 million lives since
2012, and we’re poised to achieve even greater milestones, aiming to assist 1 million individuals
annually by 2027. Our growth and success are fueled by the invaluable support of global
partners who share our vision of sustainable change. Learn more about our impactful programs
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.
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.
Application procedure
CLICK HERE TO APPLY.
Posting Date: 2026-03-12
