Data Scientist - Data Science & AI

  • Company: ASOS
  • Salary:
  • Category: Information Technology
  • Location: England London
  • Date: 4 weeks ago
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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.Job Description We are looking for a Data Scientist to join our Growth Science 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 to understand, measure and optimise Marketing budgets. 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. We will also leverage third party data from our 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 a 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 maximise 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 We'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 visualisation 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 analysing and solving problems within industry An understanding of the e-commerce marketing industry, with particular knowledge around PPC / PLA optimisation 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) #LI-BR1
Full time

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