Applied ML Lead driving production-ready AI, scalable data science, and measurable business impact across iGaming

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, causal reasoning, and model validation.

Experience designing, analysing, and interpreting experiments such as AB tests.
Experience working with SQL and large-scale datasets (e.g. in Databricks / Spark environments).
Experience designing, deploying, maintaining, or reviewing models in production or business-critical workflows.
Practical understanding of software engineering best practices relevant to machine learning, including testing, version control, code review, documentation, monitoring, and maintainability.
Experience with MLOps concepts and practices, including model lifecycle management, monitoring, retraining, deployment workflows, and production readiness.
Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
Proven experience leading or mentoring data scientists in a professional setting.
Experience working cross-functionally with product, engineering, analytics, commercial, or operational teams to deliver measurable outcomes.
Experience in iGaming, gaming, e-commerce, fintech, or another high-volume digital product environment.Strong analytical judgement and structured problem-solving ability.
Confident technical leader with the ability to challenge, guide, and improve others’ work.
Strong understanding of how to build reliable, maintainable, and production- ready machine learning solutions, not just experimental models
Clear, concise communicator able to influence both technical and non- technical stakeholders.
Comfortable making prioritisation decisions in environments with competing demands and incomplete information

Applied Machine Learning Lead

Job Description