Data Scientist – RecSys

• Design, implement, and optimize end-to-end recommendation pipelines from data ingestion to model inference
• Build and maintain scalable ETL pipelines for reliable and efficient data flows
• Develop, evaluate, and continuously improve machine learning models for recommendation systems
• Research, prototype, and implement state-of-the-art approaches to improve recommendation quality and business metrics
• Scale and optimize data and model pipelines for large volumes of data and real-time or batch processing
• Integrate behavioral, transactional, and contextual signals from multiple systems into recommendation models
• Implement unit and integration tests across data, modeling, and deployment workflows
• Monitor and maintain data pipelines, model quality, end-to-end system performance, and downstream impact
• Design and analyze A/B tests to evaluate model performance and support data-driven product decisions
• Build dashboards and observability tools for model metrics, system health, and business KPIs
• Collaborate with Data Engineers, Software Engineers, and stakeholders to deliver scalable, production-ready solutions

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

• Growth opportunities at all levels
• Constant investment in employees
• Strategic partnerships opening new opportunities for success