Learning Data Analyst (m/f/d)

About Us

We believe every child deserves the chance to reach their full potential — and that strong foundations in literacy and numeracy are where this begins. EIDU partners with governments to strengthen public education systems at scale. Our technology-enabled platform integrates proven educational interventions and supports learners, teachers, education officers, and ministry staff within national systems. Today, we work with 41 governments and will reach 1,000,000 active students across Kenya and Nigeria in 2026. We have doubled our learner numbers each year over the last five years and aim to reach tens of millions more. A randomized controlled trial found a 0.53 standard deviation improvement in learning outcomes — placing EIDU among the top 10% of education interventions in low- and middle-income countries. We combine analytical rigor, entrepreneurial drive, and disciplined execution. As we scale, we are building an organization capable of turning strategy into measurable impact at national scale.

About the role

We are looking for a Learning Data Analyst to form an integral part of the Impact team. You will own one question: What in our learner and teacher data is driving learning outcomes and where can we head next? You will use data to identify where the programme is working and where it isn't, and what that implies for the next change we should make. We regularly test changes to our product and programme through experiments. You will help design these tests from the start, using your statistical knowledge to choose the right method and work out the population and sampling approach. When the results come in, you will analyse them and explain clearly what worked and what didn’t. Between experiments, you will explore our data to find patterns that point to what we should test next. (Location: Kenya/Nigeria/Germany/India preferred, open to discuss)

What you will do:

• Link what learners and teachers do in the app, how much they use it (dosage), how many are reached (coverage) and progression related to learning outcomes.
• Diagnose where the programme is working and where it isn't, and translate findings into clear recommendations for the next product or programme change.
• Plan, design and analyse experiments end-to-end: randomisation design, power calculations, and standard causal analyses of results.
• Between experiments, run the discovery engine — funnel, cohort and fidelity analyses — to surface the next thing to test.
• Partner with our Ops/BI Analyst on shared surfaces such as dashboards and routine reporting..
• Work with the operations and learning teams to frame the right analytical questions, grounded in learning-science literacy.
• Work directly with our tech stack: Redshift, Metabase, dbt

What you should bring:

• Strong SQL and Python, with experience working directly against large, messy event-level datasets.
• Practitioner-level statistics: comfortable with experimental design, power analysis, and standard causal inference methods.
• A track record of turning ambiguous event data into clear, decision-useful analysis.
• Enough learning-science literacy to ask the right questions of learner and teacher data — formal background or applied familiarity both welcome.
• Comfort operating between rigorous experiment analysis and open-ended discovery work, and communicating both clearly to non-technical stakeholders.
• Excellent communication skills, including the ability to communicate effectively in a multi-cultural environment and communicating technical information with non-technical audiences.

This probably isn't for you if

• You want to build production ML models or data pipelines.
• You want to own dashboards and regular reporting only.
• You need clean, well-documented data before you can start.
• You want a large analytics team around you.

What we offer:

• Work on one of the biggest problems in the world: ensuring every child gets the education they deserve.
• Being a part of an international and passionate team located in Kenya, Germany and Nigeria.
• An open environment where feedback is welcomed, promoting mutual growth.
• High degree of flexibility with regard to working hours and vacations.