Data Scientist Applied ML & Recommendations

Lingokids is on a mission to help families raise amazing kids through Playlearning™. Ready to join us on this exciting journey? 🚀

Lingokids is a global leader in educational technology, helping over 185 million families worldwide raise amazing kids through Playlearning™ ,our unique approach that blends education with play. Our mission is to empower children with modern learning experiences, combining educational subjects with essential life skills to help them grow into confident, conscious, and resilient lifelong learners.

Beyond our award-winning app, we’ve built a multi-platform educational universe , including our “ Baby Bot” and “ Baby Bot’s Backyard Tales” shows, Podcasts, and Music Publishing . Our content, developed in collaboration with top education experts and Oxford Press University, ensures an engaging, high-quality learning experience in a safe, ad-free environment . This dedication to excellence has earned Lingokids multiple industry awards across app, podcast, and video categories, including Best Original Learning App by Kidscreen Awards, National Parenting Product Awards by NAPPA Awards, and Best Parenting Product by Good Housekeeping, among many others!

As a Data Scientist (ML Engineering) on the Product Engagement team, your mission is twofold: keep our recommendation infrastructure robust, scalable, and production-ready , and explore and validate more advanced recommendation algorithms that could take our personalization to the next level. Where the Data Scientist (Recommendations & Experimentation) designs the statistical logic, you are the person who makes sure it actually works in production - at scale, reliably, and fast - while also pushing the frontier of what our recommendation engine is capable of technically. Think of yourself as the engineering backbone and the technical innovator of the recommendations squad.

What you'll do

Own the production recommendation infrastructure : maintain and improve the systems that serve personalized content to millions of users, ensuring reliability, low latency, and scalability as the catalog and user base grow.

Research and prototype advanced recommendation algorithms : explore newer approaches - deep learning-based models, contextual bandits, session-based recommendations, graph-based methods - evaluate their potential, and run controlled experiments to validate uplift before production.

Produce ML models and pipelines : take prototypes (from yourself or from the team's Data Scientist) and turn them into production-grade, monitored, maintainable features integrated into the live recommendation engine.

Design scalable infrastructure : anticipate bottlenecks and design systems that can handle larger catalogs, more complex segmentations, and higher traffic - including serving layer optimization, caching strategies, and pipeline orchestration.

Build and maintain data pipelines in DBT and Databrick