Data Scientist at Educate!

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Position Overview

Educate!
is the biggest youth skills service provider in East Africa, and we're looking for a Data Scientist.We're not just looking for someone to tweak algorithms—we want someone who can shape opportunities.As our lead Data Scientist, you'll work where social science and advanced analytics meet, moving beyond simple relationships to solve the deeper "why" behind youth success.You won't just look at what's happening in our programs; you'll help shape our "Theory of Change" by uncovering the key social factors that drive improvements in young people's lives.This is a rare chance to use solid analysis to build tools that can change the lives of thousands of young people in Africa.

You'll have the chance to create and define the Data Science function from scratch.
You'll work closely with leaders across Product, Tech, and Evaluation to turn complex data insights into real-world policies.Whether you're building advanced machine learning systems or making sure your findings make sense to partners, your work will be a powerful tool for making smart, data-driven choices.If you're a practical technologist who's excited about seeing your work make a real difference, this is your next big challenge.

What You'll Do

Theory-Driven Causal Discovery

Construct Causal Frameworks: Move beyond seeing just what's connected.
You'll use behavioral science and economic theory to build "Theories of Change" that show the deeper social factors that lead to youth success.

Hypothesis-Led Feature Engineering: Don't just throw data at problems.
You'll think up and test solid hypotheses to uncover the "why" behind how well our programs are working, turning social science ideas into variables that can help predict outcomes.

Inform Product Strategy: Work with Product and Evaluation teams to find areas where data insights can change how programs are designed and delivered, helping to shape real-world solutions.


Advanced Analytics and Pragmatic Modeling

Build Outcome-Focused Models: Create and update complex models—ranging from simple rules to advanced machine learning—to predict and influence key results like student retention, livelihood improvements, and teaching methods adoption.


Analyze Heterogeneity: Look beyond average results.
You'll use advanced statistical methods to understand how different groups and situations respond to our programs.

Prioritize Impact over Complexity: Use a "Minimum Viable Model" approach to choose the best tools for the job, making sure your models are easy to maintain, scalable, and practical for use in the field.


Strategic Translation & Insight "Last-Mile"

Data Storytelling & Visualization: Turn complex data findings into clear, powerful stories and visuals that help non-technical leaders and government partners make smart decisions based on evidence.


Close the Insight-Action Loop: Work with Product and Evaluation teams to make sure our model outputs go beyond simple reports and become part of real-world tests, product updates, or strategy changes.


Decision-Support Standardization: Set up a consistent way to present data insights, ensuring that every analysis ends with a clear plan of action for stakeholders.


Building the Data Science Function

Design the Data Science Lifecycle at Educate!: Take ownership of the full Data Science process at Educate!, from starting a project to making sure the final results are used in real programs.


Build the Organizational Memory: Create a "Lessons Learned" system that records both successes and valuable mistakes, helping the team learn from every new project.


Cross-Functional Infrastructure Strategy: Work with Tech, Metrics, and RME leaders to make sure our data tools and processes keep up with more advanced needs as we grow.


Who You Are

Master's degree in a social science field (like Economics, Sociology, Psychology, or Public Policy) with a strong focus on quantitative methods, or a degree in Data Science/Statistics with substantial experience in social research.


You should be good at using R or Python for data handling and statistical work.


You must be able to write and run SQL queries on complex databases.


You should have experience with causal analysis, long-term data studies, and economic methods.


You need a proven record of designing surveys, working with real-world, messy data from developing areas, and using evaluation frameworks to measure impact.


You should be able to explain a "p-value" to a teacher and "youth agency" to a software engineer.


You should have a strong interest in education reform, youth development, and the growth opportunities in East Africa.


Technical Stack (Preferred)

Languages: Python (pandas, scikit-learn, statsmodels) or R (tidyverse, ggplot2, lme4)

Tools:

SQL: BigQuerySQL for structured data

Version Control: Git/GitHub

Field Data Collection: ODK/SurveyCTO

Data Visualization: Looker, Tableau

Unstructured/Semi-Structured Data:

Familiarity with semi-structured data (JSON, nested fields, open text fields)

Experience merging qualitative context (like program notes and transcripts) into quantitative analysis workflows

Experience with text analysis methods—thematic coding, basic NLP, or using LLMs for feature extraction

Methods: RCTs, Propensity Score Matching, Difference-in-Differences, and basic Machine Learning (Random Forests, Clustering).

Method of Application

Interested and qualified? Go to Educate! on job-boards.greenhouse.io to apply