Additionalinformation

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

Companydescription

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.

Jobdescription

Job Description

We're looking for an Applied Scientist to join the team – whose mission is to build machine learning capabilities that power better decisions, experiences and outcomes across ASOS.

You'll work on challenging real-world machine learning problems, developing scalable models and intelligent systems that support a range of business domains.

As an Applied Scientist, you'll work alongside data engineers, ML engineers, analysts, product managers and business stakeholders to design, develop and deploy machine learning solutions at scale. You'll have the opportunity to influence both the scientific direction of our ML capabilities and the products they enable.

Key Responsibilities

• Design, develop and deploy machine learning models and data-driven solutions in production environments.
• Apply machine learning 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 both industry and academia.
• Work closely with product and business stakeholders to identify opportunities where machine learning can create value.
• Contribute to the team's technical and scientific direction through knowledge sharing, code reviews and collaboration.
• Help shape best practices in machine learning, experimentation and applied research across the organisation.

Qualifications

About You

You'll enjoy applying machine learning to large-scale, real-world challenges and translating research into production systems that deliver measurable impact.

We'd be particularly interested in candidates who bring experience in some of the following areas:

• Developing and deploying machine learning models in production environments.
• Applying statistics, analytics and machine learning techniques to solve complex business problems.
• Experience in one or more of the following areas:
  • Developing and applying machine learning solutions to solve complex business problems.
  • Building predictive models, intelligent systems or decision-support capabilities using large-scale data.
  • Translating research, experimentation and analytical insights into production-ready solutions.
  • Designing and evaluating models using appropriate performance, business and customer impact measures.
  • Working across the end-to-end machine learning lifecycle, from problem definition and experimentation through to deployment and monitoring.
  • Applying quantitative, statistical or optimisation techniques to support decision-making and product development.
• Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow or similar.
• Experience working with large datasets and distributed data processing systems.
• Strong software engineering practices, including testing, version control and maintainable code.
• Ability to communicate technical concepts to both technical and non-technical audiences.
• Curiosity, pragmatism and a willingness to learn, experiment and share knowledge.
• Experience bringing ML products from ideation through to production.
• Experience working in fast-paced, product-driven environments.
• Familiarity with cloud-native ML platforms and MLOps practices.
• Publications, open-source contributions or evidence of staying current with developments in machine learning and AI.

Videos

Videos To Watch