Educate! works to transform education in Africa to teach youth to solve poverty for themselves and their communities. Educate! provides youth with skills training in leadership, entrepreneurship and workforce readiness along with mentorship to start real businesses at school. Our model is delivered through practically-trained teachers and youth mentors. E...

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Data Scientist

Job Type

Full Time

Qualification

BA/BSc/HND , MBA/MSc/MA

Experience

Location

Nairobi

Job Field

Data, Business Analysis and AI , ICT / Computer

You will have the mandate to build and codify the Data Science function from the ground up. Working directly with Global Directors across Product, Tech, and Evaluation, you will bridge the gap between complex model outputs and actionable policy. Whether you are designing sophisticated machine learning frameworks or translating high-signal narratives for partners, your work will be the an engine for evidence-based decision-making. If you are a pragmatic technologist who is driven by the desire to see your code manifest as real-world impact, this is your next career-defining challenge.

What You’ll Do

Theory-Driven Causal Discovery

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.

Advanced Analytics and Pragmatic Modeling

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.

Strategic Translation & Insight "Last-Mile"

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.

Building the Data Science Function

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.

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.

• 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.
• Ability to explain a "p-value" to a teacher and "youth agency" to a software engineer.

• 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.
• Proven track record of designing surveys, handling "messy" real-world data from emerging markets, and working with impact evaluation frameworks.