Applied Scientist

We're looking for an Applied Scientist to join one of ASOS's machine learning teams, working on high-impact problems that influence customer experience, business decision-making, and operational performance across the organisation. In this role, you'll apply machine learning, statistical modelling, and experimentation techniques to large-scale datasets, developing solutions that help ASOS better understand customers, optimise products and services, and make smarter decisions. You'll work across the full machine learning lifecycle - from problem formulation and exploratory analysis through to model development, evaluation, and production deployment. The focus is on delivering robust, scalable solutions that generate measurable business impact rather than building models in isolation. This is a highly collaborative role where you'll partner with Machine Learning Engineers, Data Engineers, Product Managers, and stakeholders to translate business challenges into practical machine learning applications. You'll contribute to both short-term impact and longer-term scientific innovation, helping shape how machine learning is applied across ASOS. You'll also play an active role in fostering a culture of experimentation, evidence-based decision-making, and technical excellence, bringing new ideas, research, and approaches into the team where appropriate. What you'll be doing

• Designing, developing and evaluating machine learning models to solve complex business problems.
• Applying statistical and computational techniques to extract insights from large and diverse datasets.
• Building and running experiments to evaluate hypotheses, validate models, and measure impact.
• Working with engineers to deploy machine learning solutions into scalable production environments.
• Selecting appropriate modelling approaches for a range of problems including prediction, optimisation, recommendation, forecasting, and causal analysis.
• Translating complex technical outputs into clear recommendations for stakeholders.
• Contributing to shared codebases, MLOps practices, experimentation frameworks, and engineering best practices.
• Keeping up to date with developments in machine learning, statistics, and applied research, and helping bring those innovations into the team.
• Supporting an open, collaborative, and inclusive team culture.