Applied Scientist (Marketing & Customer) at ASOS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

03 Jun, 26

Salary

0.0

Posted On

05 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Science, Model Deployment, Algorithm Scaling, Experiment Design, Statistical Methods, Software Development Best Practices, Programming Languages, Frameworks, Causal Inference, Bayesian Methods, MMM, Research, Code Quality Improvement, Feature Development, Collaboration

Industry

Retail Apparel and Fashion

Description

Company 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. But how are we showing up? We’re proud members of Inclusive Companies, are Disability Confident Committed and have signed the Business in the Community Race at Work Charter and we placed 8th in the Inclusive Top 50 Companies Employer list. Everyone needs some help showing up as their best self. Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you. Job Description We are seeking an Applied Scientist to join a collaborative machine learning product team focused on delivering innovative solutions that enhance the customer experience. This role offers the opportunity to work on large-scale, real-world problems and contribute to impactful projects across key business areas. The position is part of a broader Applied Science function that designs and maintains algorithms supporting various operational and customer-facing domains - with a focus on Marketing focused Data Science/Machine Learning techniques. The team builds machine learning models at scale, drawing on rich data sources to drive meaningful outcomes. Key Responsibilities Collaborate within a cross-functional team to develop and deploy large-scale machine learning systems. Lead the implementation and scaling of algorithms with measurable business impact. Design and conduct experiments to validate models and inform product direction. Stay current with developments in the field through research, reading groups, and prototype testing. Contribute to ongoing improvements in code quality, infrastructure, and feature development. Participate in learning opportunities, knowledge-sharing sessions, and technical events. Promote diversity, equity, and inclusion in both team culture and work practices. Qualifications About You Demonstrated experience applying machine learning in production environments. An interest in working on marketing specific Data Science/Machine Learning projects - Such as MMM, Incrementality/Causal Inference etc. Depending on the team's focus, relevant experience could include areas such as causal inference, or Bayesian methods. Proficiency in programming languages used in machine learning and familiarity with common frameworks. Solid grasp of statistical methods and software development best practices. Ability to work independently, manage timelines, and deliver prototypes or models aligned with business needs. Strong collaboration skills and comfort working across technical and non-technical roles. An interest in research and innovation, with any publications in reputable machine learning venues considered a plus. 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.

Responsibilities

The Applied Scientist will collaborate within a cross-functional team to develop and deploy large-scale machine learning systems focused on enhancing the customer experience, particularly in marketing domains. Key duties include leading the implementation and scaling of algorithms with measurable business impact and designing experiments to validate models.