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. 

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.

Qualifications

About You

We're keen to hear from Applied Scientists, Data Scientists, Machine Learning Scientists, or researchers who enjoy solving real-world problems using data and machine learning.

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 techniques in a commercial, research, or production environment.
• Strong knowledge of machine learning fundamentals, including model development, evaluation, and experimentation.
• Solid understanding of statistics, probability, and scientific problem-solving.
• Experience with supervised and/or unsupervised learning techniques, feature engineering, and model validation.
• Proficiency in Python and familiarity with modern machine learning frameworks and tooling.
• An appreciation for software engineering best practices and deploying production-ready solutions.
• Ability to frame ambiguous business challenges as analytical or machine learning problems.
• Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.
• Curiosity about emerging research, with an interest in applying new techniques to real-world problems.
• Experience in areas such as experimentation, causal inference, Bayesian methods, optimisation, recommendation systems, forecasting, or GenAI/LLMs would be beneficial but is 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.