Senior Catalogue Associate
As part of Amazon's Global Catalog Operations team, I specialize in Attribute Discovery - analyzing and mapping product data to enhance discoverability across the world's largest e-commerce platform. My work focuses on identifying and optimizing key product attributes that directly improve search visibility and conversion rates, ensuring customers can find exactly what they're looking for. Through data-driven analysis and alignment with Amazon's strict catalog standards, I help bridge the gap between product offerings and customer search intent, ultimately driving better shopping experiences and business results.
Catalog Management Expertise
Product Data Optimization & Strategy
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Comprehensive Product Research & Analysis:
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Conducted in-depth analysis across diverse product categories (electronics, home goods, apparel, etc.) to identify key attributes that enhance catalog display and search relevance. Evaluated product hierarchies, relationships, and taxonomies to ensure accurate classification and improved discoverability.
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Attribute Mapping & Standardization:
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Identified and mapped hundreds of product attributes (dimensions, materials, specifications) to Amazon’s global catalog standards.
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Developed structured guidelines for attribute prioritization based on customer search behavior, conversion impact, and SEO best practices.
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Collaborated with cross-functional teams (Product, Engineering, and Vendor teams) to align attribute schemas with business goals.
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Competitive Intelligence & Market Benchmarking
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Performed competitor analysis to evaluate attribute effectiveness across top e-commerce platforms (e.g., Walmart, eBay, Best Buy).
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Identified gaps in Amazon’s catalog structure and recommended attribute additions/modifications to improve search ranking and customer experience.
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Leveraged insights to influence Amazon’s attribute adoption strategy, ensuring competitive advantage in product discoverability.
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Machine Learning & Catalog Automation
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Testing & Auditing ML Models:
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Validated performance of custom Machine Learning (ML) tools designed for auto-tagging, attribute prediction, and catalog quality control.
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Flagged inaccuracies in automated attribute assignments and refined training datasets to reduce errors by X% (if applicable).
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Quality Assurance & Continuous Improvement:
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Conducted A/B tests to measure the impact of new attributes on click-through rates (CTR) and conversions.
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Audited catalog listings to ensure compliance with Amazon’s data integrity policies, minimizing misclassified products.
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