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Data Scientist – Data Science & AI 1020 views

ASOS is one of the UK’s top fashion and beauty destinations, expanding globally at a rapid pace. Their values are to be authentic, brave, and creative, and they live and breathe these in everything they do.

They believe fashion can make you look, feel, and be your best, and, with technology in their DNA, they deliver the latest trends to their digital-obsessed 20-something market. Their award-winning Tech teams sit at the heart of their business. They deliver technical innovation and pioneer incredible solutions, which are crucial to their continued success. They’re extremely ambitious and thrive on the individuality of their amazing employees. Their values encompass everything needed for their tech people to be the thought leaders of tomorrow.

Job Description

They are looking for a Data Scientist to join their Growth Science team and play a key role in helping ASOS provide the best shopping experience to their 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 produce interesting and difficult problems to understand, measure, and optimize marketing budgets.

At ASOS, as an online-only retailer, they have unique datasets – transactions and clickstreams for millions of customers and photos, videos, and text descriptions of hundreds of thousands of products. They will also leverage third-party data from their Marketing partners and various platforms.

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 strong attention to detail.

What You’ll Be Doing…

  • Working in a cross-functional team, alongside machine learning scientists, engineers, business analysts, and non-technical stakeholders, creating new and improving internal and external-facing data products
  • Driving measurable impact across the business through advanced analytics and statistical analysis – Working out where the most value is and helping set up frameworks for evaluating algorithmic improvements
  • Support and create a framework for experimentation in Geo-based experimentation, reporting on past experiments and designing new ones to maximize ROI
  • Keeping up with relevant state-of-the-art research, taking part in reading groups alongside other scientists, with the opportunity to create novel prototypes for the business, and publish at top conferences

They’d love to meet someone with…

  • A degree in Computer Science, Physics, Mathematics or a similar quantitative subject
  • A solid understanding of statistics (hypothesis testing, regressions, random variables, inference)
  • Comfortable with presenting back to technical and non-technical stakeholders through effective data visualization and building of reporting frameworks
  • Experience accessing and combining data from multiple sources and building data pipelines, including a good knowledge of SQL
  • Comfortable working in a Python data science tech stack (e.g. pandas, NumPy, scikit-learn, PySpark, PyMC3, Dash, Plotly )
  • The ability to work collaboratively and proactively in a fast-paced environment alongside both scientists, engineers, and non-technical stakeholders
  • A ‘hackers’ mentality, comfortable using open source technologies.

An Added Bonus If You Have

  • An advanced degree in Computer Science, Physics, Mathematics or a similar quantitative subject
  • Experience in using advanced statistical methods to solve problems within the Marketing / Experimentation space – This can either be through academic projects and publications, or experience analyzing and solving problems within the industry
  • An understanding of the e-commerce marketing industry, with particular knowledge around PPC / PLA optimization in the Google eco-system
  • A basic understanding of software development lifecycles, engineering, and machine learning practices (Data pipelines, API workflows, CI/CD deployments, DataOps, MLOps )

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