Growd works with parents of children below the age of 14 years by finding, designing and matching them to co-curricular activities that improve their child's academic performance in mathematics, english and science. We base this on their developmental milestones, location, interests and goals so that they can continue to learn outside the classroom at their own pace while designing their own learning experiences. We also provide a hub that they can use to get answers to their most pressing questions privately and confidently. Growd is revolutionizing how we think of education. We are a fast-paced and growing company based in London and Nairobi. You will therefore be right where the action is, and get involved in the incredible journey of growing a business!
Growd Global Ltd - Children and co-curricular Activities
Machine Learning Engineer, Data Scientist
REQUIREMENTS:
DESCRIPTION:
RESPONSIBLITIES:
1. Building algorithms to process and route information intelligently between our users
2. Help us efficiently match young learners to appropriate learning resources and information depending on their key characteristics
3. Help us map the effects of access to quality educational resources and information on young learners, your work has the potential to impact millions of lives.
4. Develop methods for message classification
5. Using smarter routing to improve user experience
6. Identifying trends within our data.
At Growd, you'll have a direct impact on millions of children by building products that connect children or young learners to quality education, services, and information they need to grow. You'll have a unique opportunity to develop Natural Language Processing (NLP) tools in multiple regional, under-resourced languages – enabling fair access to ML for all. We believe that ALL children deserve access to quality education and resources and our products should reflect that. Our product should be relevant, efficient, and intuitive: no matter your language, background, or culture. As part of this, you'll be expected to lead a culture of algorithmic fairness: understanding algorithmic bias, how it can manifest, and how to minimize it through good design.
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