Data Scientist - RecSys

WHAT YOU'LL BE DOING

• Design, implement, and optimize end-to-end recommendation pipelines, from data ingestion to model inference.
• Build and maintain scalable ETL pipelines to support reliable and efficient data flows.
• Develop, evaluate, and continuously improve ML models for recommendation systems.
• Research, prototype, and implement state-of-the-art (SOTA) approaches to improve recommendation quality and drive key business metrics.
• Scale and optimize data and model pipelines to handle large volumes of data and real-time or batch processing needs.
• Integrate multi-modal data (e.g., behavioral, transactional, and contextual signals) from various systems into recommendation models.
• Ensure robustness and stability of pipelines by implementing unit and integration tests across data, modeling, and deployment workflows.
• Monitor and maintain end-to-end system performance, including data pipelines, model quality, and downstream impact.
• Design and analyze A/B tests to evaluate model performance and support data-driven product decisions.
• Build dashboards and observability tools to track model metrics, system health, and business KPIs.
• Collaborate closely with Data Engineers, Software Engineers, and stakeholders to deliver scalable, production-ready solutions.

WHAT WE ASK OF YOU

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Strong Python experience with recent production use, including hands-on work with data science and machine learning libraries and frameworks (e.g., Pandas, Polars, NumPy, scikit-learn, PyTorch, TensorFlow, JAX, Hugging Face, …).
• Experience building and deploying end-to-end machine learning systems on cloud AI platforms (Azure, GCP, or AWS), from ETL pipelines to deployment and monitoring, including model versioning and experiment tracking, supporting either batch or real-time workflows.
• Strong understanding of deep learning–based recommender systems for next-item prediction, and analogous NLP architectures that model sequential patterns and context.
• Demonstrated experience building efficient data transformation pipelines for both transactional (OLTP) and analytical (OLAP) workloads, with strong knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, MongoDB, Cassandra).
• Experience with unit and integration testing (e.g., Pytest), CI/CD pipelines, and Docker-based containerization.