Design, build, and maintain machine learning models and AI products that are deployed in production and create measurable value for players and the business
Contribute to personalisation and decisioning systems - including recommendation engines, segmentation models, propensity scoring, and Next Best Action frameworks - that directly influence player retention and engagement
Support the development of AI agent architectures and automation workflows that extend the team’s capacity to deliver intelligence at scale
Translate complex stakeholder needs into well-scoped, pragmatic solutions, balancing model sophistication with delivery speed and operational sustainability
Collaborate closely with data analysts, data engineers, and product stakeholders to ensure models are integrated, understood, and acted upon

You will have developed a strong working knowledge of our data infrastructure, player behaviour data, and live operations context across active titles
You will have familiarised yourself with our suite of ML models spanning forecasting, propensity modelling, and player decisioning, and made meaningful contributions to at least one
You will have expanded our AI/ML programme to cover additional player engagement scenarios and franchises, including personalisation and player decisioning capabilities
You will have built trusted working relationships with key stakeholders, understanding their needs and how data science can serve them

You will be a core contributor to the evolution of our personalisation and player engagement capabilities, including our Next Best Action engine and propensity modelling programme
You will have helped shape the roadmap for new AI product opportunities, drawing on a clear view of where ML and agent-based approaches can generate near-term value for the business

At least 3 years’ experience in an applied Data Science or ML role, with a track record of shipping models or AI products into production
Demonstrated ability to deliver measurable outcomes through applied ML: your work has made it into the hands of end users, not just into a notebook
Strong collaborative instincts: you understand that impact comes from working well with others, not from working alone
Pragmatic problem-solver who can scope work appropriately, move fast where needed, and know when good enough beats perfect
Comfortable working across disciplines, from game designers and producers to analysts and engineers, with strong communication skills to match

Strong Python skills, including experience writing production-grade code beyond notebook prototypes
Solid SQL skills for data exploration, feature engineering, and pipeline work
Hands-on experience with ML frameworks such as scikit-learn, LightGBM, PyTorch, or Spark ML
Expert-level proficiency in Databricks and/or Snowflake, with experience deploying and managing models in a cloud data platform environment
Version control proficiency (git) and familiarity with collaborative development workflows

Data Scientist

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