Who You Are:

• Master’s in a Social Science field (Economics, Sociology, Psychology, Public Policy) with a heavy quantitative focus, OR a degree in Data Science/Statistics with significant experience in social research.
  
• Proficiency in R or Python for data manipulation and statistical analysis.
  • • Strong command of SQL for querying complex databases.
      
    • Experience with causal inference, longitudinal data analysis, and econometrics.
      
• Proven track record of designing surveys, handling "messy" real-world data from emerging markets, and working with impact evaluation frameworks.
  
• Ability to explain a "p-value" to a teacher and "youth agency" to a software engineer.
  
• A deep passion for education reform, youth empowerment, and the development landscape in East Africa.
  

• Construct Causal Frameworks: Move beyond correlation. You will leverage behavioral science and economic theory to develop "Theories of Change" that map the latent social mechanisms driving youth success.
  
• Hypothesis-Led Feature Engineering: Don't just throw data at a wall. You’ll formulate and test rigorous hypotheses to identify the "why" behind program performance, turning social science theory into predictive variables.
  
• Inform Product Strategy: Act as a strategic partner to Product and Evaluation teams, identifying high-leverage use cases where data-driven insights can fundamentally pivot program design or delivery.
  

• Build Outcome-Focused Models: Develop and maintain sophisticated models—from rule-based frameworks to advanced ML—designed to predict and drive key indicators like student retention, livelihood gains, and pedagogy adoption.
  
• Analyze Heterogeneity: Go deeper than "average" results. You will employ advanced statistical tactics to examine how program impacts vary across different demographics and contexts.
  
• Prioritize Impact over Complexity: Lead with a "Minimum Viable Model" mindset, selecting the right tool for the job to ensure technical solutions are maintainable, scalable, and—most importantly—useful for field operations.
  

• Data Storytelling & Visualization: Synthesize complex statistical findings into compelling, high-signal narratives and visuals that empower non-technical leaders and government partners to make evidence-based decisions.
  
• Close the Insight-Action Loop: Co-create with Product and Eval teams to ensure model outputs aren't just "reports" but are deployed as A/B tests, product experiments, or model updates.
  
• Decision-Support Standardization: Establish a unified framework for data storytelling, ensuring that every analysis cycle culminates in a clear "Go/No-Go" decision for stakeholders.
  

• Design the Data Science Lifecycle at Educate!: Own the end-to-end Data Science workflow at Educate!—from initial intake and prioritization to validation and productionalization.
  
• Build the Organizational Memory: Implement a "Lessons Learned" framework that codifies wins and "productive failures," ensuring the team’s collective intelligence grows with every model iteration.
  
• Cross-Functional Infrastructure Strategy: Collaborate with Tech, Metrics, and RME leads to ensure our data stack and pipelines evolve to support increasingly sophisticated analytical needs.