Portuguese (Brazil) Quality Control Lead
W
Welo DataAI Data Quality
Brazil / Mexico / Argentina / SpainContractLead
Salary not disclosed
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Job Details
- Languages
- Native-level proficiency in Portuguese (Brazil) and professional written and spoken English.
- Experience
- Minimum 2 years of experience in AI training, data annotation, linguistic quality, localization QA, or a related quality assurance role; at least 1 year of experience leading or supervising reviewers, annotators, or quality teams.
- Required Skills
- Quality AssuranceNLP
Requirements
- Native-level proficiency (CEFR C2 or equivalent) in Portuguese (Brazil).
- Professional written and spoken English.
- Minimum 2 years of experience in AI training, data annotation, linguistic quality, localization QA, or a related quality assurance role.
- At least 1 year of experience leading or supervising reviewers, annotators, or quality teams.
- Strong understanding of linguistic quality, including grammar, fluency, tone, register, and cultural nuances.
- Experience providing structured, evidence-based feedback and coaching others to improve quality.
- Excellent analytical skills with the ability to evaluate complex tasks.
- Strong organizational skills and the ability to manage multiple priorities in a fast-paced remote environment.
Responsibilities
- Lead and support a team of approximately 10 Quality Control Reviewers, ensuring consistent quality standards across the Portuguese (Brazil) locale.
- Review reviewer feedback, audit quality, and ensure alignment with project guidelines.
- Act as the primary quality point of contact for the Portuguese (Brazil) locale, resolving quality questions and escalating complex issues when necessary.
- Conduct calibration sessions and provide structured coaching to improve reviewer performance.
- Monitor quality trends, identify risks, and proactively address performance gaps.
- Support the onboarding and development of new Quality Control Reviewers.
- Collaborate with global Quality Teams to improve processes, documentation, and quality consistency across AI training tasks.
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