Learning Data Analyst

Key Responsibilities

• Operate and maintain the Learning Dashboard, which merges backend data from TTWF and in-app platforms 
  
• Support the implementation of predictive models for student progress, attendance, and learning risk levels.
  
• Generate weekly insight reports for internal teams to identify schools or students needing intervention.
  

2. App-Specific Analytics Monitoring

• Independently access and interpret metrics from in-app dashboards to assess real-time student engagement and learning trajectories.
  
• Reconcile in-app data with internal assessment and attendance records to validate learning trends.
  

3. School Health Index (SHI) Analytics

• Support the generation and refinement of the School Health Index (SHI), a dynamic bench-marking tool that compares school- and region-level performance.
  
• Help ensure SHI accuracy through validation with field data and stakeholder input.
  

4. Predictive Modeling & Advanced Analysis

• Analyze outputs from machine learning models built in collaboration between TTWF and MIT (using Python-based tools).
  
• Surface trends and generate flags for high-performing, at-risk, or stagnant learners and schools.
  
• Identify opportunities for content enhancement or field follow-up based on learning and engagement patterns.
  

5. Data Pipeline Development & Automation

• Manage and improve backend workflows including automated data scraping, cleaning, and structuring.
  
• Support or co-develop a centralized data warehouse to ensure seamless integration across learning, operational, and assessment datasets.
  

6. Cross-Departmental Collaboration

• Work cross-functionally to integrate findings into strategic decisions.
  
• Share insights and recommendations in a clear, visual, and non-technical format for diverse internal audiences.
  

7. Documentation & Reporting

• Document analytical processes, data flow diagrams, and methodologies for continuity and replication.
  
• Assist in the preparation of visual dashboards and insight summaries for external stakeholders and donors.
  

• 1–2 years of relevant experience in analytics, preferably in education, nonprofit, or impact-driven sectors.
  

• Some travel to areas from head office
  

Technical Skills

• Strong proficiency in Python for data extraction, modeling, and visualization (e.g., Pandas, Scikit-learn, Matplotlib).
  
• Experience with web scraping tools (e.g., BeautifulSoup, Selenium) and data integration from APIs or Google Sheets.
  
• Familiarity with data visualization platforms such as PowerBI, Tableau, or open-source dashboards (Streamlit/Dash).
  
• Understanding of machine learning model outputs, validation methods, and statistical testing.
  

Soft Skills

• Strong analytical thinking and ability to translate data into practical insights.
  
• Excellent organizational skills and attention to detail.
  
• Effective communicator with the ability to simplify technical findings for cross-functional teams.