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Position Overview The Applied Machine Learning Lead is a senior, hands-on role responsible for ensuring Data Science and Machine Learning initiatives deliver measurable business value. The role combines product ownership, roadmap management, advanced analytics, production-ready machine learning engineering, experimentation ownership, technical standards, and deep iGaming expertise. The focus is on identifying high-value opportunities, prioritising initiatives, delivering robust and scalable data science solutions, evaluating outcomes, and driving adoption of data-driven decision-making across the business. Success in this role is measured not only by model performance, but by the impact those models have on player behaviour, commercial outcomes, product performance, and the ability of the Data Science function to deliver reliable, reusable, and scalable solutions. Key Responsibilities     Lead and perform advanced commercial and business-focused analyses to support strategic decision-making.     Design, build, deploy, and maintain production-grade statistical and machine learning models, applying software engineering best practices to improve maintainability, reliability, testing, observability, and speed from experimentation to production.     Own the end-to-end experimentation lifecycle, from hypothesis definition and experiment design through implementation, validation, analysis, interpretation, and iterative optimisation.     Ensure analytical recommendations and model outcomes are grounded in statistically sound methods and commercially meaningful results.     Review, validate, and challenge analytical work, experiments, and machine learning models to ensure technical correctness, robustness, scalability, and business relevance.     Own and manage the data science backlog and roadmap, prioritising work in collaboration with stakeholders     Translate complex business problems into well-scoped initiatives with clear objectives, success criteria, and measurable outcomes.     Provide technical leadership on modelling approaches, feature engineering, validation methods, experimentation design, deployment practices, and continuous improvement of machine learning solutions     Develop reusable machine learning capabilities, analytical workflows, and technical standards to improve quality, consistency, scalability, and efficiency across the Data Science function.     Establish and promote standards for model review, documentation, feature engineering, validation, testing, monitoring, and production readiness.     Drive continuous improvement of the machine learning platform and team practices, including MLOps, AI-assisted development, automation, engineering productivity, and modern ways of building and operating ML systems.     Support and ensure adherence to governance, lifecycle management, and relevant policies and processes.     Monitor model performance, data drift, and impact; ensure appropriate retraining and continuous improvement.     Lead and manage the Data Science team, including coaching, feedback, performance discussions, and day-to-day prioritisation.     Collaborate closely with product, developers, and commercial teams to deliver data-driven solutions end-to-end     Communicate insights, trade-offs, and recommendations clearly to both technical and non-technical audiences Requirements     Extensive experience in applied data science, analytics, and machine learning.     Strong proficiency in Python.     Solid understanding of statistical modelling, predictive analytics,     experimentation, hypothesis testing, ca