Senior Coordinator, Data Scientist at eHealth Systems Africa

What you’ll do

• The Data Scientist is responsible for collecting, analyzing, and interpreting data to inform programmatic decision-making and optimize the performance of projects. The role involves working closely with program teams to ensure that data-driven insights are used to monitor project outcomes, assess impact, and identify areas for improvement:

Tool Development and Automation:

• Developing custom tools: Creating algorithms or software solutions that automate data collection, analysis, or reporting processes, saving time and reducing human error.
• Integration of Data Systems: Ensuring data collected from different project management tools and databases is integrated for easy access and comprehensive analysis.

Data Collection and Preprocessing

• Designing and managing data collection processes: Ensuring data from various sources (surveys, sensors, CRM systems, etc.) is collected efficiently and accurately.
• Data cleaning and preprocessing: Identifying missing or inconsistent data and using techniques to prepare it for analysis. This may involve transforming data formats, handling missing values, or ensuring data is structured properly for analysis.

Data Analysis and Modeling

• Statistical Analysis: Applying statistical techniques to interpret data and derive insights relevant to the project. This can include regression analysis, hypothesis testing, and predictive modeling.
• Machine Learning and Predictive Modeling: Developing and implementing machine learning models to predict outcomes or trends based on project data. These models can be used to forecast project risks, and outcomes, or to improve decision-making.
• Pattern Recognition: Using data mining techniques to detect patterns and trends in large datasets that can influence project outcomes.

Data Visualization and Reporting

• Creating data visualizations: Designing dashboards, graphs, charts, and other visualization tools to present data findings in an understandable format to stakeholders.
• Report Generation: Translating data analysis into comprehensive reports that include actionable insights. This involves simplifying complex data for decision-makers.
• Real-time Monitoring: Implementing systems that allow for real-time tracking and reporting of project metrics, performance indicators, and other critical data points.

Project Performance Monitoring:

• Development of Monitoring and Evaluation models
• Key Performance Indicator (KPI) tracking: Defining and monitoring KPIs related to project success, using data to assess how the project is performing over time.
• Risk Assessment: Using data to identify potential project risks (e.g., delays, budget overruns) and suggesting data-driven solutions to mitigate these risks.

Collaboration with Project Teams:

• Working with stakeholders: Collaborating with project managers, and other team members to understand data needs and ensure data supports project goals.
• Communicating insights: Acting as a bridge between technical data analysis and non-technical stakeholders, ensuring they understand the significance of data-driven insights for project strategy and execution.

Minimum Required Qualifications

• Education: Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Economics, or a related field. A master’s degree is a plus.

Experience:

• A minimum of 3–5 years of experience in data analysis, preferably in the international development, non-profit, or social sector.
• Experience with data management and reporting in programmatic or M&E contexts is highly desirable.

Technical Skills:

• Proficiency in data analysis software and tools such as Excel, SPSS, Power BI, TABLEAU, STATA, R, or Python.
• Strong experience in data visualization and developing dashboards for monitoring program performance.
• Data Management Skills: Experience in managing large datasets, ensuring data accuracy, and maintaining data security standards.
• Communication: Strong written and verbal communication skills, with experience in creating reports and presentations for both technical and non-technical audiences.
• Project Management: Ability to manage multiple data-related projects and deliver quality outputs within tight deadlines.
• Certifications: Certifications in data analysis, data science, or related fields (e.g., Google Data Analytics, Tableau, Microsoft Power BI) are a plus.