eHealth Africa designs and implements data-driven solutions and technologies to improve health systems for and with local communities. eHA’s technology works in low connectivity settings and uses data to drive decision-making by local governments and partner agencies to get optimum results.
We are recruiting to fill the position below:
Job Title: Senior Coordinator, Data Scientist
Location: Abuja
Purpose of the position
We are looking for a Senior Data Scientist with the skills, experience, and mindset to drive innovation in applied analytics, modeling, and decision intelligence. The ideal candidate is expected to build and implement real-world data use cases in areas such as health campaigns, demographic forecasting, climate risk modeling, and food security assessment. You will be responsible for driving high-impact analytical projects across multiple domains, developing models and insights that support both operational and strategic goals.
What you’ll do
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. Other duties may be assigned:
Data Management:
Conduct spatial analysis and develop geospatial models to identify patterns, trends, and relationships within the data.
Oversee data collection, storage, and retrieval processes to ensure accuracy, security, and availability.
Develop and enforce data management policies, procedures, and standards.
Manage data governance initiatives to ensure compliance with legal and regulatory requirements.
Develop dashboards, reports, and data visualizations to present insights to stakeholders.
Perform data mining, statistical analysis, and predictive modeling to address complex business questions.
Collaborate with cross-functional teams to identify data-driven opportunities for improvement.
Data Engineering and Integration:
Work closely with data engineers to improve data pipelines, data quality, and feature engineering.
Assist in the development of robust data architectures and MLOps pipelines.
Implement data quality frameworks to monitor and enhance the reliability of datasets.
Domain-Specific Modeling Use Cases:
Senior Coordinator, Data Scientist: Develop models to identify underserved populations, forecast disease spread, and support vaccine or intervention microplanning.
Demography: Build spatial demographic models using satellite imagery, census microdata, and administrative boundaries for population forecasting and resource targeting.
Climate Analytics: Analyze weather, hydrological, and satellite-derived datasets (e.g., ERA5, CHIRPS, WRF output) to model environmental risks such as heatwaves, flooding, and droughts.
Food Security: Integrate crop indices, remote sensing, market data, and socio-economic indicators to predict food insecurity trends and design resilience strategies.
Team Collaboration and Leadership:
Lead and mentor a team of data analysts and engineers, fostering a collaborative and innovative work environment
Mentor junior data scientists and analysts, providing code reviews and guidance on best practices.
Lead internal knowledge-sharing sessions and stay current with industry trends and tools
Who you are
Master’s or PhD in , Statistics, Applied Mathematics, Data Science, Public Health, demography, GIS or a related field.
Minimum of proven seven (7) years of experience in data science or applied analytics roles.
Proficiency in Python and/or R for statistical modeling and analysis.
Experience with cloud platforms (e.g., AWS, Azure, or GCP) and big data tools (e.g., Hadoop, Spark) including model deployment (e.g., SageMaker, Vertex AI)
Strong understanding of data modeling, databricks, ETL processes, (Apache Spark or PySpark, Apache Beam, dbt (Data Build Tool),Ruffus / Bonobo / Luigi)
Experience with demographic data sources (e.g., DHS, WorldPop, HRSL, GHS-POP) and climate datasets (ERA5, CHIRPS, MODIS, NASA POWER).
Exposure to humanitarian or development contexts (e.g., WHO, UN, NGOs, or government agencies) is highly desirable.
Domain experience in [e.g., public health, geospatial, research].
Working knowledge of geospatial analytics or unstructured data (e.g., images, text).
Experience with A/B testing, causal inference, or time-series forecasting.