Learning Data Analyst

Key Responsibilities

1. Dashboard Management 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.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Quantitative Economics, or a related field; Master’s degree preferred. Years of Employment Experience : 1–2 years of relevant experience in analytics, preferably in education, nonprofit, or impact-driven sectors. Travel or Location-Specific Requirements: Some travel to areas from head office P referred Experience, Background, or Skillset 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.