Applied Scientist

We are looking for an Applied Scientist to join one of our machine learning product teams and play a key role in helping ASOS provide the best shopping experience to our millions of customers. The role offers broad exposure to machine learning technologies at ASOS, requiring innovative solutions and close collaboration with business domains. 

The role sits within the Applied Science domain, which is responsible for the algorithms that power the ASOS digital ecosystem. The current focuses of these teams are Recommendations and Search, Marketing and Customer, Pricing and Forecasting, however we are actively exploring new problem spaces. Each one of these teams drive key operating decisions. Our teams maintain, build and innovate in some of the most interesting areas of machine learning at scale, training models on unique datasets, transactions and clickstream data.

What you’ll be doing

• You will be part of an agile, cross-functional team building and managing a large-scale machine learning systems, working with massive amounts of data, and delivering models into production.  
• You will be driving the implementation and scale-up of algorithms for measurable impact across the business and set up and conduct large-scale experiments to test hypotheses and drive product development. 
• You will be keeping up to date with relevant state-of-the-art research, taking part in reading groups alongside other scientists, with the opportunity to publish novel prototypes for the business at top conferences. 
• You will be continually developing and improving our code and technology, taking an active role in the conception of brand-new features for our millions of global customers.  
• You will take part in regular Tech Develops days to learn new things, take part in internal and external hackathons, and share your knowledge and help drive improvements in science and engineering. 
• You will support our culture by championing Diversity, Equity & Inclusion strategies.

About You

• You have professional experience in machine learning and their practical applications in production environments.
• Depending on the team we might expect you to have expertise in Bayesian statistics, deep learning, forecasting, causal methods, optimization and recommender systems.
• You are comfortable working in Python and familiar with at least one deep learning framework (e.g., PyTorch, TensorFlow).
• You have a solid understanding of software development lifecycles and engineering practices, alongside a good understanding of ML and statistics.  
• You can work independently to manage projects and deliver prototypes against a timeline.
• The ability to work collaboratively and proactively in a fast-paced environment alongside both scientists, engineers, and non-technical stakeholders.
• We are keen to speak to people who are comfortable with R&D, and would love to meet someone who has authored publications in top-tier machine learning conferences or journals (e.g. NeurIPS, ICLR, ICML, KDD, CVPR, ICCV, ECCV, ACL, EMNLP).