Description review
Data Scientist - RecSys
Arrise · Portugal · back to the listing
HR standards
60/100
needs work
Title ↔ description
74/100
solid
Reads as
Machine Learning Engineer
92% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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Startups
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What the listing never says
- 26 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
What you will do
• 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 you bring
• 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
What sets you 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).
About us
ARRISE is a global leader in software development and services for the iGaming industry, bringing together more than 2,500 professionals operating across more than 12 locations worldwide.
From concept through to delivery, we combine technical expertise, creativity and industry knowledge to develop products, platforms and services that shape player experiences and support some of the industry's leading brands.
We are best known for the work we deliver for Pragmatic Play, creating and supporting technology, products and specialists services enjoyed by millions of players worldwide.
Originally posted on Himalayas
• 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 you bring
• 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
What sets you 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).
About us
ARRISE is a global leader in software development and services for the iGaming industry, bringing together more than 2,500 professionals operating across more than 12 locations worldwide.
From concept through to delivery, we combine technical expertise, creativity and industry knowledge to develop products, platforms and services that shape player experiences and support some of the industry's leading brands.
We are best known for the work we deliver for Pragmatic Play, creating and supporting technology, products and specialists services enjoyed by millions of players worldwide.
Originally posted on Himalayas