Data Scientist - RecSys

About the role

In this role, you will design and optimize recommendation pipelines that enhance user experiences in the iGaming sector. You will work with large volumes of data and integrate various data types into your models, ensuring robust and efficient data flows.

Collaboration is key as you will work closely with Data Engineers and Software Engineers to deliver scalable solutions. Success in this position means continuously improving ML models and effectively monitoring system performance to drive business metrics.

Skills that matter here

• Python: You will utilize Python extensively for data science and machine learning tasks.
• Machine Learning: The role involves developing and optimizing machine learning models specifically for recommendation systems.
• Data Science: Your data science expertise will be crucial for analyzing and improving model performance.
• ETL: You will build and maintain ETL pipelines to ensure efficient data ingestion and processing.
• SQL: Strong SQL skills are necessary for managing and transforming data across various databases.
• Deep Learning: Experience with deep learning techniques will be essential for developing advanced recommender systems.

Who this role suits

• Candidates should have a strong background in computer science or engineering.
• They must be collaborative and able to work effectively within a team.
• A proactive approach to problem-solving and a passion for continuous learning are essential.
• Ideal applicants will have hands-on experience with machine learning systems in a production environment.

From the employer

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.