Optimizing these capabilities for the complexity of information about our products, the breadth and diversity of our catalog, in a way that supports Wayfair's global growth, pose key challenges. Further, product substitutability is difficult and potentially costly to measure as our products are not commodity goods that are repeatedly purchased (e.g. the customer needs the perfect sofa for their living room). We build generalized, unified models in the form of rich, concise representations to address these challenges and power decision making across Wayfair. These are used as i) features for other science models predicting customer engagement with our products, ii) through scalable search systems for discovering exact or substitutes across our catalog or our competitors', and iii) validation of substitution to drive end-to-end efficiency in product sourcing, on-site recommendations, and distribution. Wayfair is one of the world's largest online destinations for the home.

Wayfair's Data Science team builds the algorithmic systems that drive our business, enhance customer experience, and improve customer loyalty. The Data Science Catalog team at Wayfair develops machine learning models to: (1) improve customer experience by accurately tagging products using multimodal data sources (image, text, categorical, numeric, etc.), (2) drive catalog and pricing strategy by powering comparisons between large numbers of products, and (3) solve the product cold-start problem to enable customers to find the best new products. Data science products are heavily reliant on the quality of training data. We are looking for an experienced technical project manager to establish and oversee the ecosystem for label data collection and data quality assurance. Wayfair is one of the world's largest online destinations for the home.