Applied Scientist (Machine Learning)

As an Applied Scientist at ASOS, you will build scalable ML capabilities that power better decisions, experiences and outcomes across the business. You’ll collaborate with data engineers, ML engineers, analysts and stakeholders to design, develop and deploy models at scale. You influence the scientific direction of our ML capabilities and the products they enable, shaping best practices and experimentation across the organisation. This is a hands-on role with real-world impact in a fast-paced retail environment.

• employee discount (ASOS discount)
• employee sample sales
• 25 days paid annual leave + extra day
• private medical care
• Fixed Annual Payment in addition to salary
• personalised learning opportunities

• Design, develop and deploy machine learning models and data-driven solutions in production environments
• Apply ML and optimisation techniques to solve complex business problems
• Partner with engineers to productionise models and build reliable, scalable ML systems
• Design and analyse experiments and evaluation frameworks to measure model performance and business impact
• Explore, evaluate and prototype new approaches from industry and academia
• Work closely with product and business stakeholders to identify opportunities where ML can create value
• Contribute to the team's technical and scientific direction through knowledge sharing and collaboration
• Help shape best practices in ML, experimentation and applied research across the organisation

• Developing and deploying ML models in production environments
• Applying statistics, analytics and ML techniques to solve business problems
• Building predictive models or intelligent systems using large-scale data
• Translating research and insights into production-ready solutions
• End-to-end ML lifecycle experience from problem definition to monitoring
• Quantitative, statistical or optimisation techniques to support decision-making and product development
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow)
• Experience with large datasets and distributed data processing
• Strong software engineering practices (testing, version control, maintainable code)
• Ability to communicate technical concepts to diverse audiences
• Curiosity, pragmatism and willingness to learn and share knowledge
• Experience bringing ML products from ideation to production
• Experience in fast-paced, product-driven environments
• Familiarity with cloud-native ML platforms and MLOps practices
• Publications or open-source contributions or staying current with ML/AI developments

• Curiosity
• Pragmatism
• Willingness to learn and share knowledge
• Python programming
• PyTorch or TensorFlow
• Experience with production ML systems