Data Scientist

What you'll do - Data Science & Advanced Analytics: Design and deliver data science solutions including predictive modelling, forecasting, segmentation, optimisation, experimentation and measurement to support better business decisions and measurable outcomes. - Data Preparation & Analysis: Build, transform and analyse complex datasets using Python, SQL, Databricks and modern analytics tools to create robust analytical datasets and scalable workflows. - Machine Learning & Statistical Modelling: Develop, evaluate and monitor machine learning and statistical models, applying best practices across feature engineering, model validation, documentation and performance assessment. - Experimentation & Commercial Insight: Support A/B testing and measurement initiatives, helping to define hypotheses, assess results and quantify the commercial impact of business decisions and interventions. - Stakeholder Partnership & Storytelling: Work closely with business stakeholders and senior data professionals to translate complex analytical findings into clear, compelling recommendations that drive action. - Cross-Functional Collaboration: Partner with Data Engineers, Analytics teams and platform specialists to productionise analytical solutions, improve scalability and ensure operational reliability. - Continuous Improvement & Data Science Innovation: Contribute to reusable analytical assets, coding standards and best practices while identifying opportunities to automate processes, improve analytical quality and increase adoption of data science solutions across the business. What you'll bring - Data Science Experience (2-5 Years): Experience applying data science, machine learning, statistical modelling and advanced analytics techniques to solve real-world business problems and deliver measurable outcomes. - Python, SQL & Analytical Expertise: Strong Python and SQL skills, with experience building robust, reproducible analyses and working with large datasets in modern analytics environments such as Databricks, Spark or cloud-based platforms. - Machine Learning & Statistical Foundations: Solid understanding of supervised and unsupervised learning, feature engineering, model evaluation, experimentation, A/B testing and statistical analysis techniques. - Business Problem Solving & Commercial Mindset: Ability to translate business questions into analytical approaches, testable hypotheses and practical solutions that support commercial decision-making and value creation. - Data Storytelling & Stakeholder Engagement: Proven ability to communicate complex analytical findings through clear narratives, visualisations and recommendations that influence technical and non-technical stakeholders. - Collaboration & Delivery: Experience working effectively across Data Science, Analytics, Engineering and business teams, balancing analytical rigour with pragmatic delivery in fast-paced environments. - Continuous Learning & Innovation: Demonstrated curiosity for emerging technologies, data science techniques and AI capabilities, with a commitment to quality, simplicity and continuous improvement.