ABOUT US

ARRISE powering Pragmatic Play is a leading content provider to the iGaming and Betting Industry, offering a multi-product portfolio that is innovative, regulated and mobile-focused. Pragmatic Play strives to create the most engaging and evocative experience for customers globally across a range of products, including slots, live casino, sports betting, virtual sports and bingo. 

We are seeking a highly skilled Data Scientist specializing in Recommendation Systems to join our global Data Science team and drive the development, evaluation, and optimization of machine learning models in production. 
You will help build large-scale, production-grade recommendation systems that impact millions of players worldwide, working closely with Data Scientists, Software Engineers, and Data Engineers.
If you thrive in a fast-growing, high-impact environment and enjoy designing and improving systems at scale, we encourage you to apply. Even if you don’t meet every requirement, your skills and ability to deliver impact are what matter most.
 

KEY RESPONSIBILITIES

• 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.

REQUIRED SKILLS AND QUALIFICATIONS

• 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

WHAT WILL SET YOU UP APART

• Experience building large-scale recommender systems (e.g., candidate generation, ranking, retrieval, personalization)
• Track record of publications in deep learning at relevant conferences or journals
• Experience with Azure Data Factory / AWS Glue / Google Cloud Dataflow
• Experience designing and analyzing A/B tests, with a solid understanding of relevant evaluation metrics
• Experience designing and implementing metadata-driven pipelines to scale automated A/B 
  testing systems
• Experience developing multi-modal models that integrate multiple data types (e.g., text, images, audio)
• Experience applying transformer-based models or large language models (LLMs) to 
  recommendation or personalization tasks
• Experience with distributed training, including data parallelism and model parallelism
• Experience with distributed data processing and big data technologies (e.g., Spark, Hadoop, Flink, Kafka, Hive, Presto, Databricks)

WHAT WE OFFER

• Competitive compensation based on your experience and impact.
• Opportunities for professional and personal development.
• Work on state-of-the-art machine learning infrastructure and systems at scale.
• Opportunities to contribute to open-source projects and stay active in the ML community.
• Opportunity to make a measurable and visible impact within a large-scale organization.
• Flexible working hours and remote-friendly setup