Machine Learning Manager - Catalog Duplicates

About the role

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.

Responsibilities

• 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.
• Lead ML System Design Across Detect → Review → Resolve
• Oversee the design and evolution of ML models that detect exact and near-duplicate relationships at scale, using a combination of representation learning, similarity search, graph-based methods, and large language models.
• Work with partners to modernize the Review layer – including GenAI-augmented auto-review, human-in-the-loop queues, and QA workflows – so that detection output is converted into high-quality decisions efficiently.
• Collaborate closely on the Resolution/Consolidation layer to ensure that confirmed duplicates are merged or blocked automatically wherever possible, with clear contracts between science, product, and tooling.
• Define and refine measurement frameworks (e.g., detection precision/recall, review accuracy, resolution rate, time-to-resolution, net catalog reduction, and customer/supplier outcomes) and ensure they are used to steer the roadmap.
• Build, Lead, and Develop a High-Performing Science Team
• Manage and grow a team of Machine Learning Scientists working across the Duplicates work-stream, spanning model development, experimentation, and productionization.
• Provide hands-on technical leadership: review project proposals, model designs, experiment plans, and code; step into the details when the team is tackling particularly complex or high-risk problems.
• Coach scientists on end-to-end ownership – from problem scoping and stakeholder communication through launch, monitoring, and iteration – raising the bar for scientific rigor and business impact.
• Partner with recruiting and other Catalog Science leaders to hire, onboard, and develop diverse talent at multiple levels.
• Drive Cross-Functional Execution and Change
• Act as a primary science point-of-contact for Duplicates across Catalog, Merchandising, Operations, and Partner teams; proactively communicate progress, risks, and trade-offs.
• Work with engineering counterparts to ensure that model and data architectures are robust, observable, and cost-efficient, and that platform investments (feature pipelines, training/inference infrastructure, evaluation tooling) unlock reuse across deduplication use cases.
• Collaborate with catalog operations and vendor partners to design workflows that integrate ML decisions into human review and consolidation processes, with clear feedback loops back into science.
• Champion best practices for experimentation, evaluation, and model governance in a domain where mistakes have direct customer, supplier, and financial impact.

Requirements

• Experience leading applied machine learning teams in industry, with a track record of delivering production systems that drive meaningful business outcomes.
• Strong background in machine learning, statistics, or a related quantitative field, with the ability to dive deep on model design, data quality, and evaluation methodology.
• Prior experience managing and developing ML Scientists (or closely related roles) and operating as a people manager at a senior level.
• Demonstrated success driving cross-functional programs that span science, engineering, product, and operations.

Qualifications

• Proficiency in Python and the modern ML ecosystem (e.g., PyTorch, TensorFlow, XGBoost, similarity search/ANN libraries, or graph-based methods).
• Experience building and deploying models on large, messy datasets, ideally including some combination of structured attributes, text, and images.
• Familiarity with modern MLOps and data platforms (e.g., cloud infrastructure such as GCP/AWS/Azure; feature stores; experiment tracking; model monitoring).
• Comfort working with non-deterministic or human-in-the-loop systems (e.g., GenAI-assisted review, reviewer queues, or semi-automated decisioning) and designing robust evaluation frameworks for them.
• Hands-on experience using and customizing GenAI systems (both commercial APIs and open-source models), including fine-tuning, adapter/prompt design, and rigorous evaluation for production use cases.

Skills

• Ability to translate ambiguous, cross-cutting problems (like “reduce catalog duplication”) into clear, staged scientific workstreams with crisp ownership and milestones.
• Strong written and verbal communication skills; you can explain complex technical tradeoffs to non-technical stakeholders and influence decisions at multiple levels of the organization.
• Experience balancing short-term delivery (e.g., backlog reduction, acute quality issues) with longer-term architectural and platform investments.
• A growth mindset and a bias toward action – you are comfortable iterating, learning from data, and adjusting course as the problem and ecosystem evolve.

Benefits & Perks

• Time Off
• Paid Holidays
• Unlimited Paid Time Off (PTO)
• Health & Wellness
• Full Health Benefits (Medical, Dental, Vision, HSA/FSA)
• Life Insurance
• Short Term & Long Term Disability
• Global wellbeing offerings such as gym/fitness discounts and mental health support
• Financial Growth & Security
• 401(k) matching (Employee Matching Program)
• Tuition reimbursement
• Financial health education resources and tax-advantaged accounts
• Family Support
• Family planning support
• Parental leave
• Global surrogacy & adoption policy
• Professional Development & Recognition
• Rewards & recognition programs
• Global employee anniversary awards
• Paid volunteer opportunities
• Unique Perks
• Employee discount
• Local perks in select office locations
• Team and pod outings