Data Scientist – RecSys

🔥 1 minute ago

🇵🇹 Portugal – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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ARRISE

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🎮 Gaming

🤝 B2B

Gaming • B2B

ARRISE is a global software developer and services company focused on the iGaming industry. With more than 11,000 professionals across 14 locations, they design and build casino products including award-winning slot games, immersive live-casino experiences, and high-performance platform solutions that scale from concept through execution. ARRISE combines creative game design and technical engineering to drive player engagement and support operators and partners in the online gambling sector.

📋 Description

• 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-scale data and real-time or batch processing • Integrate behavioral, transactional, contextual, and other multi-modal data into recommendation models • Implement unit and integration tests across data, modeling, and deployment workflows • Monitor and maintain data pipelines, model quality, 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

🎯 Requirements

• 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, or 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 machine learning workflows • Strong understanding of deep learning–based recommender systems for next-item prediction • Understanding of analogous NLP architectures that model sequential patterns and context • Demonstrated experience building efficient data transformation pipelines for transactional (OLTP) and analytical (OLAP) workloads • Strong knowledge of SQL and NoSQL databases, such as PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, MongoDB, or Cassandra • Experience with unit and integration testing, such as Pytest • Experience with CI/CD pipelines and Docker-based containerization • Experience building large-scale recommender systems is an advantage • Publications in deep learning at relevant conferences or journals are an advantage • Experience with Azure Data Factory, AWS Glue, or Google Cloud Dataflow is an advantage • Experience designing and analyzing A/B tests and understanding relevant evaluation metrics is an advantage • Experience designing and implementing metadata-driven pipelines to scale automated A/B testing systems is an advantage • Experience developing multi-modal models integrating text, images, audio, or other data types is an advantage • Experience applying transformer-based models or LLMs to recommendation or personalization tasks is an advantage • Experience with distributed training, including data parallelism and model parallelism, is an advantage • Experience with distributed data processing and big data technologies such as Spark, Hadoop, Flink, Kafka, Hive, Presto, or Databricks is an advantage