Role: gNS Data Quality Team – Data Quality Checks Specialist
Job Summary
The Data Quality Check Specialist role supports the development of AI models and the integrity of Company & People Databases through rigorous quality assessments. Work involves evaluating AI-generated outputs and validating large data sets to ensure accuracy, consistency, and adherence to quality standards.
Accountabilities
- AI/Machine Learning Output QualityInvolved in the development of machine learning (ML) and artificial intelligence (AI) models by leveraging language mastery and subject area expertise. Individuals in this role will be responsible for performing quality assessments of GenAI outputs such as document summaries, search results, AI chat responses, and draft reports focused on company research use casesUnderstand and identify GenAI hallucinations via human testing and validation
- Company Data QualityInvolved in the quality assessment of our Company & People Databases by regularly scoring large data sets. Individuals in this role will have to pay intensive attention to scoring rules and methods to ensure we have solid quality numbers overtime. There may be additional Company & People data reviews focused on specific sources, specific companies.Perform quality assessment checks of company and personnel data from Excel.Implement and maintain rigorous quality control measures within the QA team to uphold data integrity standards.
Qualifications
- Language mastery: English minimumAdditional languages like Dutch, German, French, Chinese or Portuguese a plusLanguage minimum is subject to change based on project
- Business degree or equivalent preferred
- Good expertise with Excel and shared files
- Ability to become familiar with other software tools, such as Prodigy and Azure Dev Ops
- Subject Area ExpertiseCompany research use cases including, but not limited to, Corporate Development, Sales Intelligence/Business Development, Competitive Intelligence and Market Intelligence. This is important to ensure the AI/ML model is biased correctly to the targeted customer persona(s).Strong knowledge of business/company information (company financials, public filings, company press releases, earnings calls, ESG ratings etc) and places to get this information.
- Attention to detail – A strong attention to detail is needed in this role – completeness/accuracy of data labelling/annotation is important to the successful development/testing of models.
- Adaptability – Individuals in this role need to be comfortable with change as this field is changing rapidly.
- Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.
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