Data Scientist (Applied ML & Recommendations) (EdTech)

TL;DR

Data Scientist (Applied ML & Recommendations) (EdTech): Maintaining and scaling recommendation infrastructure for a global educational platform with an accent on production-grade ML pipelines and advanced personalization algorithms. Focus on implementing deep learning models, optimizing serving layers, and ensuring system reliability at scale.

Location: Remote (must be within 2 hours of GMT+1/Spain timezone) or Hybrid in Madrid. EU visa sponsorship is available.

Company

hirify.global is a global leader in educational technology that utilizes a unique Playlearning™ approach to provide safe and engaging learning experiences for millions of families worldwide.

What you will do

• Own and optimize the production recommendation infrastructure to ensure reliability and low latency for millions of users.
• Research and prototype advanced ML algorithms, including deep learning, contextual bandits, and graph-based methods.
• Develop production-grade ML models and pipelines, translating prototypes into maintainable live features.
• Design scalable architecture to handle growing catalogs and traffic, optimizing caching and serving layers.
• Build and maintain robust data pipelines using DBT and Databricks.
• Monitor model health in production, managing retraining strategies and detecting drift.

Requirements

• Strong Python skills for writing production-quality, testable, and version-controlled ML code.
• Proficiency in SQL and hands-on experience with DBT for transformation pipelines.
• Experience deploying and monitoring ML models on AWS (SageMaker, Lambda, ECS, Step Functions).
• Ability to design and maintain scalable batch ML training and evaluation pipelines.
• Familiarity with advanced recommendation techniques such as two-tower models and transformers.
• Experience with orchestration tools (Airflow, Prefect, or Dagster) and CI/CD workflows.
• Fluency in English (spoken and written) is essential.
• Must be based within 2 hours of Spain's timezone (GMT+1).

Nice to have

• Experience with real-time or low-latency serving layers like Redis or DynamoDB.
• Knowledge of ML experimentation frameworks, including A/B tests and counterfactual evaluation.
• Experience with modern data stack tools such as Snowflake, BigQuery, or Fivetran.
• Exposure to knowledge graph or content graph approaches for recommendations.

Culture & Benefits

• €2,000 annual budget for professional development, books, and training.
• Equity through stock options.
• Remote setup support: €400 initial allowance and €35 monthly remote work expense.
• Meal allowances via Cobee (€60/month).
• Private health insurance through Adeslas and free language classes in Spanish and English.
• Visa sponsorship and cost coverage for candidates needing a permit to work in the EU.