ASOS

E-Commerce

AppliedScientist

£75–110k ~AI est. London, England, United Kingdom FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for mid candidates.

The Brief

“Applied Scientist at ASOS. Skills: Machine learning, Statistical modelling, Causal inference, Experimentation. Design machine learning models. Develop machine learning models”

What You'll Achieve.

Shape customer acquisition; Shape customer engagement; Shape customer retention; Optimise marketing strategy; Optimise customer experience; Inform high-stakes decisions; Measurable business impact

Industry & Context.

E Commerce
Problems you'll solve

Root cause analysis

What They're Looking For.

Must Have

Experience applying machine learning, Experience applying statistical methods, Proficiency in programming language, Familiarity with ML frameworks, Solid understanding of statistical concepts, Solid understanding of software development best practices, Ability to manage work, Ability to collaborate with others, Ability to deliver models, Ability to deliver prototypes, Comfortable working with technical stakeholders, Comfortable working with non-technical stakeholders

Nice to Have

PhD preferred, Specific ML framework experience, Cloud platform certs

What You'll Do.

Design machine learning models

Develop machine learning models

Design statistical models

Develop statistical models

Work on causal problems

Deploy models into production

Translate analytical outputs

Contribute to codebases

Contribute to experimentation frameworks

Stay current with ML developments

Stay current with statistics developments

Bring ideas into team

How You'll Work.

Team & Collaboration

Partner with engineers; Partner with product managers; Partner with stakeholders; Collaborate with engineers; Collaborate with product managers; Collaborate with stakeholders; Contribute to shared codebases; Contribute to experimentation frameworks; Contribute to best practices; Support open culture; Support collaborative culture; Support inclusive culture

Communication Scope

Actionable insights

Process & Methodology

Roadmap planning

Full Job Description

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 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,

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