Applied Scientist
Applied Scientist
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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. Thats why our approach to working together includes spending at least 2 days a week in the office. Its 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 teams 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
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
