Applied Scientist

We’re ASOS, the online retailer for fashion lovers all around the world. We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you’re free to be your true self without judgement, and channel your creativity into a platform used by millions. Everyone needs some help showing up as their best self. We're Disability Confident Committed \- Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you. **Job Description** We’re looking for an Applied Scientist to join a **Customer \& Marketing‑focused machine learning team** , working on problems that directly shape how ASOS acquires, engages, and retains customers. In this role, you’ll apply statistical and machine learning techniques to optimise marketing strategy and customer experience \- from understanding what drives customer behaviour to evaluating the true impact of marketing activity. You’ll work with rich, large\-scale customer and behavioural datasets, designing models and experiments that inform high‑stakes decisions across the business. These include areas like attribution, incrementality, and marketing mix modelling, where the focus is not just prediction, but causal understanding and measurable business impact. This is a highly applied role \- your work will move beyond analysis into production‑ready solutions, helping the business make better decisions with confidence. You’ll partner closely with engineers, product managers, and stakeholders to ensure your models are robust, scalable, and embedded into real workflows. You’ll also contribute to a growing experimentation culture at ASOS, where decisions are grounded in data and rigorously tested \- whether through A/B testing, causal inference frameworks, or econometric modelling approaches. **What you'll be doing** * Designing and developing machine learning and statistical models that drive marketing and customer decision‑making * Working on causal problems such as attribution, incrementality, and marketing effectiveness * Building and running experiments to evaluate models and inform product and business strategy * Collaborating with engineers to deploy models into scalable, production environments * Translating complex analytical outputs into clear, actionable insights for stakeholders * Contributing to shared codebases, experimentation frameworks, and best practices * Staying current with the latest developments in applied ML and statistics, and bringing relevant ideas into the team * Supporting an open, collaborative, and inclusive team culture **Qualifications** **About You** We’re keen to hear from Applied Scientists or Data Scientists who are curious, thoughtful about their work, and enjoy collaborating with others. You don’t need to meet every requirement below to apply if the role sounds interesting and aligns with your experience or career goals, we’d encourage you to apply. * Experience applying machine learning or statistical methods in a practical or production‑adjacent setting * An interest in marketing‑focused data science or machine learning problems (e.g. causal inference, incrementality, MMM) * Experience or interest in areas such as causal inference, Bayesian methods, or experimentation * Proficiency in at least one programming language commonly used in machine learning, and familiarity with relevant frameworks * A solid understanding of statistical concepts and software development best practices * Ability to manage your work, collaborate with others, and deliver models or prototypes aligned with business needs * Comfortable working with both technical and non‑technical stakeholders * An interest in research and innovation; publications or conference contributions are welcome but not required **Additional Information** **BeneFITS’** * Employee discount (hello ASOS discount!) * Employee sample sales * 25 days paid annual leave \+ an extra celebration day for a special moment * Private medical care scheme * Fixed Annual Payment in addition to your salary each year, it's just an extra thank you from us * Opportunity for personalised learning and in\-the\-moment experiences that enable you to thrive and excel in your role.