Data Scientist, Recommender Systems – iGaming

ARRISE; Portugal; Presencial; EUR 55 000 - 75 000; Tempo integral

Resumo da oferta

ARRISE is recruiting for a Machine Learning Engineer to design and optimize end-to-end recommendation systems within its iGaming focus. You will build scalable ETL and ML pipelines, integrate multi-modal data, and continuously improve model quality to drive key business metrics.

The role involves collaboration with Data & Software Engineers to deliver production-ready solutions, with exposure to cloud platforms (Azure/AWS/GCP) and modern ML frameworks.

Qualificações

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Responsabilidades

• Design, implement, and optimize end-to-end recommendation pipelines, from data ingestion to model inference.
• Build and maintain scalable ETL pipelines for reliable data flows.
• Develop, evaluate, and improve ML models for recommendation systems.
• Research and implement state-of-the-art approaches to improve recommendation quality and business metrics.
• Scale and optimize data and model pipelines for large data and real-time or batch processing.
• Integrate multi-modal data from various systems into models.

Conhecimentos

Python

Data science libraries

ML models

Cloud platforms

Deep learning for recommender systems

SQL & NoSQL

CI/CD testing (Pytest)

Docker containerization

Formação académica

Bachelor's or Master's in CS/Engineering

Ferramentas

Azure

AWS

GCP

Spark

Hadoop

Flink

Kafka

Snowflake

Databricks

PyTorch

TensorFlow

JAX

Hugging Face

Descrição da oferta de emprego

ARRISE is recruiting for a Machine Learning Engineer to design and optimize end-to-end recommendation systems within its iGaming focus. You will build scalable ETL and ML pipelines, integrate multi-modal data, and continuously improve model quality to drive key business metrics.

The role involves collaboration with Data & Software Engineers to deliver production-ready solutions, with exposure to cloud platforms (Azure/AWS/GCP) and modern ML frameworks.