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I will clean, validate, and QA ai datasets with high accuracy
India
I speak English
I will clean, validate, and QA AI datasets with high accuracy
I am an AI Data Analyst with 5+ years of professional experience in AI-generated datasets, event tagging, data validation, and quality assurance. I have worked with global sports analytics teams where...
About this Gig
I provide professional AI dataset cleaning, validation, and quality assurance to help you improve data accuracy and reliability.
With 5+ years of real-world experience working on AI-generated datasets, event tagging, and QA workflows, I focus on identifying errors, inconsistencies, and quality gaps that can affect analysis or model performance.
What I can help you with:
- AI dataset cleaning and formatting
- Data validation and consistency checks
- Review and QA of AI-generated annotations
- Error analysis (false positives / negatives)
- Excel-based data cleaning and reporting
- Python-supported validation and automation
Why choose me:
- Experience with production-level AI and analytics data
- Strong attention to detail and structured QA approach
- Clear communication and on-time delivery
- Honest scope and no overpromising
Please message me before placing an order so we can confirm dataset size, format, and expectations.
Technology:
Excel
•
Google Sheets
•
Python
•
VBA
•
Zapier
•
PowerShell
FAQ
Q: What type of data do you work with?
A: I work with AI datasets including CSV, Excel, Google Sheets, and structured annotation outputs. This includes image, video, text, and event-based data.
Q: Do you build AI or machine learning models?
A: No. My focus is on data cleaning, validation, and QA, which is critical before model training or analysis.
Q: How do you ensure data accuracy?
A: I use systematic validation checks, manual review, and automation (Excel/Python) to identify inconsistencies, missing values, and errors.
Q: Will my data remain confidential?
A: Yes. All client data is handled confidentially and never shared.
Q: What do you need from me to start?
A: Dataset files, brief description of the task, expected output, and any specific rules or validation criteria.
