Machine Learning Scientist hos ASOS.com


London
ASOS Technology is going through an exciting period of transition and major investment. – this includes a number of strategic programmes to deliver the amazing technology and business solutions to support our ambitious global growth plans. At the heart of these plans is the rebuilding of our digital platforms and channels to provide the best shopping experience for our customers. Our plan is designed to enable us to really put our mobile experience first, enable personalisation and support a data driven organisation. We are also making significant investments in all our Buying, Merchandising, Finance and People systems with the latest toolsets and applications to accelerate the next phase of our global growth. We are also improving our ways of working within Technology to enable autonomous platform development and improve our engineering and agile practices.
ASOS is one of the UK’s top fashion and beauty destinations, expanding globally at a rapid pace. Our values are to be authentic, brave and creative, and we live and breathe these in everything we do.
We believe fashion can make you look, feel and be your best and, with technology in our DNA, we deliver the latest trends to our digital-obsessed 20-something market. Our award winning Tech teams sit at the heart of our business. We deliver technical innovation and pioneer incredible solutions, which are crucial to our continued success. We’re extremely ambitious and thrive on the individuality of our amazing employees. Our values encompass everything needed for our tech people to be the thought leaders of tomorrow.
We are looking for Machine Learning Scientists to join our team and play a key role in helping ASOS provide the best shopping experience to our millions of customers. The role offers broad exposure to ASOS, requiring close collaboration with retail, marketing and technology divisions. You will be part of a highly innovative AI platform working alongside engineers and fellow scientists to solve and productionise interesting and difficult problems and leveraging cutting edge technology. At ASOS, as an online only retailer, we have unique datasets – transactions and click streams for millions of customers and photos, videos, and text descriptions of hundreds of thousands of products.
The ideal candidate will have a strong technical background and experience solving tough problems with large datasets. You will be a highly intelligent self-starter, able to work independently with a strong attention to detail.
Whatyou'llbedoing...
  • Working in cross functional team, alongside engineers, business analysts and non-technical stakeholders, creating new and improvinginternal and external facing data products
  • Drivingtheimplementationandscale-upofalgorithmsformeasurable impact across the business
  • Setting up and conducting large-scale experiments to test hypotheses and drive product development.
  • Keeping up 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

We'd love to meet someone with...

  • An advanced degreein Computer Science, Physics, Mathematics or a similar quantitative subject - a Ph.D. is a bonus
  • Experience in using machine learning methods to solve problems involving complex/high - dimensional data (e.g. image, click - streams, text, video, speech, time series) - This can either be through a distinguished academic career alongside relevant publications, or significant experience solving and productionising models within industry
  • An understanding of the retail, marketing, and/or ecommerce industry
  • Comfortable working in a Python data science tech stack (e.g. pandas, NumPy, Dask, scikit-learn, Keras, PySpark, PyTorch). Additional knowledge of Scala is desirable
  • Experience accessing and combining data from multiple sources and building data pipelines, including a good knowledge of SQL
  • Understanding of software development lifecycles and engineering practices (Data pipelines, API workflows, CI/CD deployments) alongside ML (DataOps, MLOps)
  • A solid understanding of statistics (hypothesis testing, regressions, random variables, inference)
  • The ability to work collaboratively and proactively in a fast-paced environmentalongside both scientists, engineers and non-technical stakeholders
  • A ‘hackers’ mentality, comfortable using open source technologies.

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