About The Position

As a Data Scientist on the Shopbop/Zappos Catalog Tech team, you will design and implement scientific approaches to revolutionize how we manage and enhance our product catalog data for our world-class selection of Shoes, Kids, and Active wear. You will work with Zappos' Senior leadership team to solve complex data challenges through advanced analytics and machine learning - creating innovative solutions and influencing product decisions through data-driven insights. You will lead critical initiatives to reduce catalog errors, accelerate product data capture, and develop state-of-the-art image classification systems for fashion features. You will partner daily with engineering teams and business stakeholders to provide expert guidance on model selection and implementation. As a member of the Zappos technical staff, you will leverage machine learning technologies and have access to industry leaders in AI/ML and E-Commerce to help grow your expertise. You will also routinely collaborate with data science teams across our sister companies at Amazon.com and Shopbop.com. You will push the boundaries of what's possible with applied machine learning and bring innovative solutions to bear for customers (including computer vision, NLP, and advanced ML models). You will think big about how data science can transform our catalog operations and be persistent in delivering robust, scalable solutions.

Requirements

  • 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 4+ years of data scientist experience
  • Experience with statistical models e.g. multinomial logistic regression
  • Bachelor's degree

Nice To Haves

  • 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
  • Experience managing data pipelines
  • Experience as a leader and mentor on a data science team

Responsibilities

  • Design and implement machine learning approaches to improve catalog data quality.
  • Develop and validate scientific methodologies for automated data capture and classification.
  • Partner with engineering teams to integrate ML models into production systems.
  • Create and present analysis that drives decision-making at the senior leadership level.

Benefits

  • Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.
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