Data Scientist Recommender Systems

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.

• 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.