Applied Scientist

ASOS; London; Hybrid; GBP 50,000 - 70,000; Full time

Job summary

A leading online fashion retailer in London is seeking an Applied Scientist to join its team. The role focuses on developing large-scale machine learning solutions that enhance customer experience across various domains. Key responsibilities include collaborating with cross-functional teams, implementing algorithms, and designing experiments. Candidates should have demonstrated experience in machine learning application and strong programming skills. This position offers competitive compensation and performance-related bonuses, along with professional development opportunities.

Benefits

Competitive compensation

Professional development support

Generous paid leave

Flexible benefits allowance

Qualifications

• Experience in production environments applying machine learning.
• Familiarity with machine learning frameworks.
• Ability to manage timelines and deliver aligned prototypes.

Responsibilities

• Collaborate with a cross-functional team on machine learning systems.
• Implement and scale algorithms for business impact.
• Design experiments to validate models.

Skills

Machine learning application

Deep learning

Programming languages proficiency

Statistical methods

Collaboration skills

Job 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. These include recommendations, search, marketing, pricing, and forecasting, with the scope continuously evolving to address new challenges. 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.

We believe being together in person helps us move faster, connect more deeply, and achieve more as a team. That's why our approach to working together includes spending at least 2 days a week in the office. It's a rhythm that speeds up decision-making, helps ASOSers learn from each other more quickly, and builds the kind of culture where people can grow, create, and succeed.

Qualifications

About You

• Demonstrated experience applying machine learning in production environments.
• Depending on the team's focus, relevant experience could include areas such as deep learning, forecasting, optimization, recommender systems, 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'

• Competitive compensation and performance-related bonuses
• Professional development and career growth support
• Generous paid leave, including additional personal celebration days
• Flexible benefits allowance
• Access to learning resources and internal knowledge-sharing events
• Employee perks and wellness support options