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# Data Scientist Recommender Systems

- **Company:** EyeSpy Recruitment
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Standard Sql, Data Streaming, Pytorch, Apache Spark, Deep Learning, Apache Flink, Real Time Data, Apache Kafka, Machine Learning Operations
- **Published:** October 24, 2026
- **Apply:** https://www.jobleads.com/es/job/data-scientist-recommender-systems--spain--e97254e49457c19b726d1c04780c58f84

## About the Role

* Around 3-5 years of hands-on experience in machine learning, with real work on recommender systems, personalisation or ranking
 * Strong Python and SQL
 * Deep-learning experience with PyTorch, TensorFlow or JAX
 * Models you've taken to production on AWS, GCP or Azure
 * The ability to explain what you built, why, and what changed because of it

## Description

Data Scientist, Recommender Systems | Remote | Python, PyTorch, Deep Learning

Millions of players. Thousands of games. One question: what should they play next?

We're hiring a Data Scientist specialising in Recommender Systems for a fast-growing global gaming technology company. You'll build the machine-learning models that decide what millions of players discover, and you'll see your work go live, not sit in a notebook.

What you'll actually do

 * Build recommendation models. Candidate generation, retrieval and ranking that make every player's lobby feel personal.
 * Predict what's next. Use sequential behaviour, user and item embeddings and deep learning for next-item prediction.
 * Ship to production. Take models from data and experiments through deployment and monitoring, then keep improving them.
 * Prove it works. Run offline evaluation and A/B testing, and connect model quality to real product results.
 * Try new things. Test transformer-based recommendation, two-tower models and multimodal signals where they genuinely help.

What you'll bring

 * Around 3-5 years of hands-on experience in machine learning, with real work on recommender systems, personalisation or ranking
 * Strong Python and SQL
 * Deep-learning experience with PyTorch, TensorFlow or JAX
 * Models you've taken to production on AWS, GCP or Azure
 * The ability to explain what you built, why, and what changed because of it

Two-tower models, learning to rank, ANN search, cold start, session-based recommendations, MLOps, Spark, Kafka or Flink. Experience from e-commerce, streaming, media, marketplaces or ad tech counts. Your job title matters less than what you've built.

Why you'll like it

 * Real scale: your models reach millions of players worldwide.
 * Real ownership: you own problems from idea to production.
 * Real growth: learn alongside data scientists, ML engineers and data engineers who ship.
 * Modern stack: deep learning, cloud ML and real-time data, with room to test new ideas.