Senior Data Scientist

We’re hiring a Senior Data Scientist (Individual Contributor) to join our UK Retention & Data Science team. This is a hands-on role focused on driving player retention and long-term value across our UK brands by turning data into production-ready models and decisioning tools. Working closely with the Lead Data Scientist, you’ll partner with stakeholders to understand business problems, shape the analytics approach, and build solutions that are adopted, measured, and iterated over time.

You’ll own the end-to-end delivery of data science solutions: data preparation and feature engineering, model development and evaluation, deployment and monitoring. You’ll apply statistical modelling, machine learning, and experimentation to create high-quality predictive systems that support customer targeting, personalisation, and marketing optimisation. A core part of the role aside from building predictive models is causal inference: using rigorous methods to quantify the true impact of business changes on KPIs, and to design and improve promotional and marketing policies that drive measurable customer and commercial outcomes.

We’re looking for someone who can operate with autonomy, communicate clearly with technical and non-technical partners, and combine strong statistical foundations with practical Python engineering to ship reliable solutions. Our environment is cloud-first, with a core stack including AWS and Snowflake, and a culture that values learning, collaboration, and impact.

You will be responsible for the following main activities:

• Partner with stakeholders to identify high-impact retention and marketing opportunities, translate business questions into analytical problem statements, and define success metrics.
• Own end-to-end delivery of data science solutions from discovery and prototyping through to production deployment, monitoring, and iteration.
• Develop and deploy predictive models and decisioning tools (e.g., churn/retention, propensity, lifetime value, next-best-action, offer targeting) that are integrated into live customer journeys and operational processes.
• Design, implement, and analyse experiments (A/B and multivariate testing), ensuring robust statistical methodology, appropriate guardrails, and clear interpretation and presentation of results to stakeholders.
• Build incrementality and causal measurement frameworks to quantify the true impact of interventions and business changes on key KPIs (e.g., retention, engagement, NGR), and guide promotional and marketing policy decisions.
• Lead feature engineering and data preparation using Python, SQL, and the analytics stack (Snowflake/AWS), producing reliable, reusable datasets and features for modelling and experimentation.
• Establish model evaluation and monitoring practices (performance, drift, calibration, bias/segmentation checks), and refine models based on feedback and observed outcomes.
• Communicate insights and recommendations clearly to technical and non-technical audiences, translating analyses into practical actions and trade-offs.