Data Scientist, Lingokids

• Python for ML and infrastructure: strong Python skills applied to model training, evaluation, deployment, and pipeline scripting. Writes production-quality, testable, version-controlled code - not just notebooks
• SQL and DBT: solid SQL and hands-on DBT experience to build and maintain reliable transformation pipelines with clear data lineage and quality controls
• ML production on AWS: hands-on experience deploying and monitoring ML models using AWS services (SageMaker, Lambda, ECS, Step Functions). Understands model drift, monitoring strategies, and retraining triggers
• Batch ML model training and evaluation pipelines: design, build, and maintain scalable machine learning training and evaluation pipelines that support recommendation systems and related personalization use cases. This includes developing robust, well-monitored workflows for model development, deployment, and continuous improvement, while contributing to the evolution of the recommendation infrastructure toward more adaptive and responsive systems over time
• Advanced ML algorithms: familiarity with recommendation techniques beyond collaborative filtering - e.g. neural approaches (two-tower models, transformers for sequences), contextual bandits, learning-to-rank. Knows how to evaluate and compare them rigorously
• Orchestration and CI/CD: experience with orchestration tools (Airflow, Prefect, or Dagster) for reliable, observable pipelines, and comfort with Git and CI/CD workflows for ML systems
• Scalability and system design mindset: can anticipate infrastructure bottlenecks, reason through architecture trade-offs (batch vs. streaming, horizontal vs. vertical scaling), and connect engineering decisions to business outcomes
• English is a must: We’re a multicultural team providing a service in English, so while certifications aren’t necessary, fluency is essential
• As a fully remote company, clear and effective spoken and written communication, especially in asynchronous and long-form formats, is key to collaborating successfully

Desirable

• 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
• 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 Databricks, ensuring clean transformations, data quality, and robust experimentation frameworks that the team can rely on
• Monitor model health in production: define retraining strategies, detect drift, and ensure recommendation quality is measured and maintained over time
• Collaborate closely with the Data Scientist and Senior Analyst to translate statistical insights and business requirements into engineering decisions