Lead Data Scientist (m/f/d)

at  EIDU GmbH

ABOUT US

We believe that all children should be given the chance to reach their full potential. However, more than 600 million children in the world do not achieve foundational skills like literacy and basic numeracy – despite the majority of them going to school.
At EIDU, we have built an education platform designed to work for children in low- and middle-income countries (LMIC) who do not have access to the high-quality education that all children deserve. We provide tools for students, teachers, teacher trainers and governments to capacity build across the entire education system. An externally run randomized control trial has found we are in the top 10% of effective LMIC education interventions in the world.
One of the core elements of EIDU’s approach to improving outcomes for our students is Learning Engineering. The vast anonymised data generated by our large user population creates the opportunity to systematically measure the evolution of learning in underrepresented populations and identify how we can best improve this. We use this to build AI-systems that are designed to work in our environments, this includes traditional decision making systems as well as more advanced generative systems.
With a mission like EIDU’s, diversity of thought and background within our team is crucial. We value those who can bring a diverse perspective to our platform and help us provide high-quality education to as many children as we can across the globe.
If you’d like to learn more about us, visit our website at eidu.com or watch our two-minute video.

• Be the technical lead for a Data Science team dedicated to creating innovative solutions that benefit our population; This includes building personalisation algorithms so each student is working with the most impactful content for them.
• Collaborate with our Learning team to deploy generative models such as LLMs that can generate new content that is culturally relevant and effective.
• Design features which can meaningfully communicate a student’s learning to their teachers.
• Co-ideate and co-design on new ways in which Data Science can accelerate learning outcomes. in lower income environments.
• Track the impact of your work through randomised control trials and A/B testing.