Wayfair

Machine Learning Manager - Catalog Duplicates

Boston, MA, US$297,250-$300,000Posted 28 days ago

Job Description

Candidates for this position are preferred to be based in Boston, MA and will be expected to comply with their team's hybrid work schedule requirements. Our team's are in office Tuesday, Wednesday, Thursday and remote on Monday and Fridays.

Who We Are

Wayfair is an online retail platform with the mission to enable everyone to live in a home they love. Delivering on that mission at global scale requires a high-quality, trustworthy product catalog that customers and internal systems can rely on.

The Catalog Health Science organization builds the machine learning systems that power catalog quality end-to-end: how products enter the catalog, how they are structured, and how they are presented to customers. Within this group, the Duplicates program focuses on one of our most foundational problems: identifying and resolving duplicate and option-variant listings across tens of millions of products, and preventing new duplicates from ever reaching the site.

Our north star is an orchestrated system that Detects, Reviews, and Resolves duplicates end-to-end with minimal human touch, while protecting customer trust, reducing supplier friction, and improving operational efficiency.

We are looking for a Manager of Machine Learning Science to own the science strategy and execution for the Duplicates program across detection, review, and consolidation.

What You’ll Do

Set Strategy & Direction for the Duplicates Program

  • Define and own the ML/AI strategy for product deduplication across across the product lifecycle, and aligned with broader ML/AI team roadmaps
  • Translate an ambitious north star (automated end-to-end deduplication) into a sequenced set of deliverables that balance impact, risk, and technical complexity.
  • Partner with product, engineering, analytics, and catalog operations leaders to prioritize work, shape problem definitions, and align on success metrics for duplicate prevention, backlog reduction, and catalog quality.

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