Product & Manufacturer Data Validation Specialist

Location: Noida / Hyderabad

Experience: 3 to 5 Years

No. Of Openings: 2

Job Description

We are seeking a detail-oriented and analytical Product & Manufacturer Data Validation Specialist to support our data quality initiatives for industrial parts. This hybrid role combines responsibilities for validating manufacturer information and ensuring accurate product taxonomy classification. The ideal candidate will work with AI-generated data, Excel workbooks, and online resources to verify and standardize manufacturer names and product categories, enhancing the integrity of our product and supplier databases.

Manufacturer Data Validation

• Review manufacturer names extracted via AI-assisted web search and assess accuracy.
• Conduct manual research to confirm manufacturer identities and correct discrepancies.
• Update Excel workbooks with verified and standardized manufacturer data.
• Flag ambiguous or unverifiable entries for further investigation.
• Provide feedback to improve AI data extraction and enrichment workflows.

Product Taxonomy Validation

• Validate and refine product taxonomy for industrial parts, ensuring alignment with internal classification standards.
• Review AI-generated or vendor-supplied categorizations and correct misclassifications.
• Research product attributes to determine appropriate category placement.
• Document taxonomy rules, exceptions, and updates for reference and training.
• Support taxonomy mapping for new product onboarding and catalog expansion.

Job Specification

• Strong proficiency in Microsoft Excel, including formulas, filters, and data management tools.
• Excellent research and analytical skills with a high attention to detail.
• Experience working with AI-generated data or web scraping tools is a plus.
• Familiarity with manufacturer databases, industrial parts, or technical product data.
• Understanding of product taxonomy, classification systems, and metadata.
• Ability to work independently and manage multiple tasks effectively.
• Strong communication skills for cross-functional collaboration.