Senior Applied Scientist

We're looking for a Senior Applied Scientist to join our AI Demand Forecasting team. Our mission is to build forecasting capabilities that power critical business decisions across the company. While our foundations are in replenishment forecasting, we're evolving into a forecasting platform that provides scalable, high-quality demand forecasts for a growing range of use cases, including AI-powered Pricing and Supply Chain Optimisation. This means solving complex machine learning challenges while developing reusable forecasting capabilities that can be applied across multiple business domains. As a Senior Applied Scientist, you'll help shape the scientific direction of our forecasting platform, leading the design of advanced machine learning solutions that combine statistical modelling, deep learning and multimodal AI to solve challenging forecasting problems. You'll work closely with ML Engineers, data engineers, analysts, product managers and business stakeholders to translate research into production systems and create measurable business impact. Responsibilities • Lead and collaborate on the design, development and evaluation of machine learning models for demand forecasting and related AI products. • Shape and support improvements in forecasting accuracy, robustness, scalability and explainability across multiple business domains. • Develop reusable modelling approaches that support a growing forecasting platform serving replenishment, pricing, supply chain optimisation and future AI products. • Research, prototype and evaluate modern and emerging machine learning techniques from academia and industry, identifying opportunities to improve forecasting performance. • Design rigorous offline and online evaluation methodologies to measure both model quality and business impact. • Write, test and maintain production-quality Python code for machine learning models and forecasting pipelines, following software engineering best practices to support reliable deployment and long-term maintainability. • Work closely with ML Engineers to productionise models, ensuring scientific approaches can operate reliably and efficiently at scale. • Provide technical leadership and guidance on complex modelling projects, helping shape scientific direction and investment decisions. • Mentor and support other scientists through technical guidance, code reviews, experimentation best practices and knowledge sharing. • Communicate scientific findings and recommendations clearly to both technical and non-technical stakeholders.