Data Scientist - Recsys

Arrise; Lisboa; Presencial; EUR 60 000 - 100 000; Tempo integral

Resumo da oferta

ARRISE is seeking a skilled Machine Learning Engineer to design and implement end-to-end recommendation pipelines and scalable ETL workflows. You will develop, evaluate, and continuously improve ML models for our high‑volume analytics environment.

You'll collaborate with Data Engineers and Software Engineers to deliver production‑ready solutions on cloud platforms, leveraging deep learning and NLP architectures to boost recommendation quality and business metrics.

Qualificações

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Strong Python experience with data science and ML libraries (Pandas, Polars, NumPy, scikit‑learn, PyTorch, TensorFlow, JAX, Hugging Face).
• Experience building and deploying end-to-end ML systems on cloud AI platforms (Azure, GCP, AWS), including ETL, deployment, monitoring, versioning, experiments.
• Strong understanding of deep learning-based recommender systems and NLP architectures for sequential patterns.
• Data transformation pipelines for OLTP and OLAP, with SQL and NoSQL (PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, MongoDB, Cassandra).
• Experience with unit/integration testing (Pytest), CI/CD, and Docker.

Responsabilidades

• Design, implement, and optimize end-to-end recommendation pipelines from ingestion to inference.
• Build and maintain scalable ETL pipelines for reliable data flows.
• Develop, evaluate, and improve ML models for recommendation systems.

Conhecimentos

Python

ML pipelines

NLP architectures

Experiment tracking

CI/CD

Formação académica

Bachelor's degree in Computer Science

Master's degree in a related field

Ferramentas

Pandas

Polars

NumPy

scikit-learn

PyTorch

TensorFlow

JAX

Hugging Face

Azure

GCP

AWS

SQL

NoSQL

Descrição da oferta de emprego

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, 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? Your journey starts here.

WHAT YOU'LL BE DOING

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 WE ASK OF YOU

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 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) torecommendation 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).