Zappos Data Scientist II, Zappos​/Shopbop Catalog Engineering

Description

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  and  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.

Key job 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.

A day in the life

You start the day reviewing model performance metrics, noting some drift in the image classification system that needs investigation. You spend the morning developing a new approach to reduce product attribute errors using recent advances in LLMs. In the afternoon, you meet with engineering teams to advise on model architecture for a new feature, and wrap up by analyzing the results of your latest A/B test on data capture efficiency improvements.

About the team

Zappos/Shopbop Catalog Tech team owns the software that drives our photostudio, product cataloging, and integration to Amazon's marketplace. We use Amazon's Leadership Principals and Engineering Expertise but have our own fun vibe. We are located in Madison WI, and Las Vegas NV.

Basic Qualifications

* 2+ years of data scientist experience

* 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

* 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

* 1+ years of guiding and coaching a group of researchers experience

* 1+ years of working with or evaluating AI systems experience

* 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience

* Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

* Experience applying theoretical models in an applied environment

Preferred Qualifications

* Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)

* Knowledge of machine learning concepts and their application to reasoning and problem-solving

* Experience in Python, Perl, or another scripting language

* Experience in a ML or data scientist role with a large technology company

* Experience in defining and creating benchmarks for assessing GenAI model performance

* Experience working on multi-team, cross-disciplinary projects

* Experience applying quantitative analysis to solve business problems and making data-driven business decisions

* Experience effectively communicating complex concepts through written and verbal communication

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