Data Scientist - RecSys

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Quick Summary

Overview

ABOUT US ARRISE sets the benchmark for service delivery and excellence in the iGaming industry. Playing a key role in the success of its clients, which include Pragmatic Play,

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Data ScientistData

ARRISE sets the benchmark for service delivery and excellence in the iGaming industry. Playing a key role in the success of its clients, which include Pragmatic Play, a brand relied upon by the world’s biggest online casinos for its cutting-edge products, ARRISE helps to deliver exceptional gaming experiences to millions of players worldwide.

Our global team of over 12,000 talented and driven professionals are shaping the future of iGaming. Headquartered in Gibraltar, we have offices spanning Canada, India, the Isle of Man, Latvia, Malta, Romania, Serbia, Bulgaria, and the UAE, and more exciting destinations on the horizon.

At ARRISE, we take pride in creating growth opportunities at all levels, constantly investing in our people while welcoming new colleagues and forging strategic partnerships that open new opportunities for success.

To achieve this, we bet on ourselves. We know that success is a collective effort, and our team is driven by ambition, collaboration, and a shared commitment to grow and succeed — while embracing every step of the journey.

Be part of the future of iGaming with 12,000 ARRISERS! See a job that excites you? Apply now, and our friendly recruitment team will connect with you soon. Your journey starts here.

Responsibilities

~1 min read

→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.

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

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).