Applied Scientist

About ASOS

ASOS is a global online fashion retailer serving style-conscious customers across the world. The company aims to create an environment where people can be themselves, feel respected, and use their creativity to make an impact on a platform reached by millions. ASOS also supports candidates who need adjustments during the hiring process as part of its Disability Confident commitment.

Role overview

ASOS is hiring an Applied Scientist to join a machine learning team focused on business-critical work that affects customer experience, operational efficiency, and decision-making across the company. The role involves using machine learning, statistics, and experimentation to work with large datasets and create solutions that improve understanding of customers, enhance products and services, and support better decisions.

This position covers the complete machine learning workflow, from defining the problem and analysing data to building, validating, and deploying models into production. The emphasis is on creating reliable, scalable systems that deliver measurable business value, not just developing models in isolation.

The role is collaborative in nature and involves close partnership with Machine Learning Engineers, Data Engineers, Product Managers, and other stakeholders. You will help convert business problems into practical machine learning solutions while contributing to both immediate outcomes and longer-term scientific advancement. The team also values experimentation, evidence-based decisions, and technical quality, so you will be encouraged to bring in relevant ideas and research.

Key responsibilities

• Create, refine, and assess machine learning models that address complex commercial challenges.
• Use statistical and computational approaches to uncover patterns and insights in large, varied datasets.
• Plan and execute experiments to test assumptions, assess models, and quantify business impact.
• Collaborate with engineering teams to take machine learning solutions into scalable production systems.
• Choose suitable modelling methods for tasks such as prediction, optimisation, recommendation, forecasting, and causal analysis.
• Explain technical findings in a way that leads to clear, actionable recommendations for stakeholders.
• Contribute to shared repositories, MLOps workflows, experimentation tooling, and engineering standards.
• Stay current with developments in machine learning, statistics, and applied research, and help introduce useful innovations to the team.
• Promote a team environment that is open, cooperative, and inclusive.

Requirements

• Background in applying machine learning in a commercial, research, or production setting.
• Strong understanding of core machine learning concepts, including model building, evaluation, and experimentation.
• Good grasp of statistics, probability, and scientific problem-solving.
• Hands-on experience with supervised and/or unsupervised learning, feature engineering, and model validation.
• Proficiency in Python along with familiarity with modern machine learning frameworks and tools.
• Understanding of software engineering best practices and experience with production-ready deployment considerations.
• Ability to convert unclear business problems into analytical or machine learning use cases.
• Strong communication skills and the ability to work well with both technical and non-technical audiences.
• Interest in emerging research and enthusiasm for applying new techniques to practical problems.
• Helpful experience in experimentation, causal inference, Bayesian methods, optimisation, recommendation systems, forecasting, or GenAI/LLMs is a plus but not mandatory.
• Applicants do not need to meet every requirement to be considered.

Perks and benefits

• Staff discount on ASOS products.
• Access to employee sample sales.
• 25 days of paid annual leave, plus an additional celebration day for a special occasion.
• Private medical care coverage.
• A fixed annual payment provided alongside salary each year as a thank-you benefit.
• Personalised learning opportunities and real-time experiences to help you grow and perform well in the role.

Additional information

ASOS encourages people from varied backgrounds to apply and values individuality, creativity, and inclusion in its workplace. Candidates who need support or adjustments during the recruitment process can inform the talent team so suitable arrangements can be made.