Data Entry & Product Dataset Specialist
Budget: ₹600 – ₹1,500 INR
We are looking for a Data Entry Specialist with strong experience in dataset creation,
cleaning, and enrichment to help build a comprehensive skincare product database.
The role will be divided into three phases:
1. Data Collection – Manually gathering verified product data from official brand websites.
* Visit the official websites of skincare brands such as The Ordinary, Nykaa,
* Neutrogena, and other assigned brands
* Manually extract detailed product information for all products listed on each brand’s
* website.
* Ensure all entries are authentic, verified, and not generated or guessed.
* Deliverable - A structured CSV file for each brand containing all the available products with all fields filled
2. Data Cleaning – Cleaning and organizing both newly collected and existing product
* Clean and standardize data formats for both datasets (newly collected and existing ~1,000-product dataset).
* Ensure consistency in:
* Text capitalization, spacing, and formatting
* Category naming conventions
* Units and quantities (e.g., ml, g)
* Price format and currency
* Remove duplicates and redundant entries.
* Validate that all image URLs, brand names, and references are correct and active.
* Maintain data integrity — no fields should be changed without verification.
* Deliverable - Cleaned, consistent, and verified master product dataset in CSV format combining both new and existing data. A short cleaning summary (noting duplicates removed, inconsistencies corrected, etc.).
3. Missing Field Completion – Filling in missing or incomplete information in the existing dataset by cross-referencing reliable online sources.
* Cross-check product details from: Official brand websites, Verified e-commerce platforms (e.g., Nykaa, Amazon, Sephora, etc.), Other credible skincare information sources
* Manually fill in missing fields such as: Ingredient List, Active Ingredients, How to Use, Benefits, Product Claims
* Deliverable - Updated version of the existing dataset with all missing fields completed. Documentation of sources used for verification.
cleaning, and enrichment to help build a comprehensive skincare product database.
The role will be divided into three phases:
1. Data Collection – Manually gathering verified product data from official brand websites.
* Visit the official websites of skincare brands such as The Ordinary, Nykaa,
* Neutrogena, and other assigned brands
* Manually extract detailed product information for all products listed on each brand’s
* website.
* Ensure all entries are authentic, verified, and not generated or guessed.
* Deliverable - A structured CSV file for each brand containing all the available products with all fields filled
2. Data Cleaning – Cleaning and organizing both newly collected and existing product
* Clean and standardize data formats for both datasets (newly collected and existing ~1,000-product dataset).
* Ensure consistency in:
* Text capitalization, spacing, and formatting
* Category naming conventions
* Units and quantities (e.g., ml, g)
* Price format and currency
* Remove duplicates and redundant entries.
* Validate that all image URLs, brand names, and references are correct and active.
* Maintain data integrity — no fields should be changed without verification.
* Deliverable - Cleaned, consistent, and verified master product dataset in CSV format combining both new and existing data. A short cleaning summary (noting duplicates removed, inconsistencies corrected, etc.).
3. Missing Field Completion – Filling in missing or incomplete information in the existing dataset by cross-referencing reliable online sources.
* Cross-check product details from: Official brand websites, Verified e-commerce platforms (e.g., Nykaa, Amazon, Sephora, etc.), Other credible skincare information sources
* Manually fill in missing fields such as: Ingredient List, Active Ingredients, How to Use, Benefits, Product Claims
* Deliverable - Updated version of the existing dataset with all missing fields completed. Documentation of sources used for verification.