Quality Control Associate for Data Training - Bangladesh

Join a dynamic and fast-growing AI infrastructure company that collaborates with Fortune 100 clients to shape the future of machine learning and artificial intelligence. As a Data Quality Control Associate/Specialist, you will play a pivotal role in maintaining the quality of data annotation, ensuring customer success, and contributing to a seamless AI development process.

### Your Profile
- Bachelor’s degree (Business, Technology, Data Science, or related field).
- 2+ years in data quality, QA/QC, annotation operations, or comparable quality-focused roles.
- Strong understanding of data/image preprocessing and annotation workflows; comfortable with detailed guidelines and edge cases.
- Proven ability to meet quality KPIs (accuracy, consistency, defect rate) and maintain high attention to detail in repetitive reviews.
- Strong analytical skills for trend analysis, sampling, and root-cause investigation; able to turn findings into process improvements.
- Excellent written communication for reports, documentation, and stakeholder updates; Minimum C1 English proficiency required.
- Familiar with collaboration tools (e.g., Slack/Discord) and spreadsheets/dashboards; basic Python or scripting is a plus.
- Prior experience in AI data training / data labeling projects is preferred.
- Reliable, self-managed, and comfortable working remotely on an hourly contractor basis.

### Key Responsibilities
- Develop a comprehensive understanding of data annotation platforms, tools, and workflows, and ensure top-tier quality in annotated datasets across projects.
- Meet or exceed quality control KPIs for assigned tasks and projects.
- Shadow and support team leads, project coordinators, and QC specialists to learn best practices for data quality assurance.
- Maintain clear, consistent communication with team members, clients, and service partners to ensure smooth operations and transparent updates.
- Assist in onboarding and training new team members on tools, platforms, and processes.
- Conduct in-depth analyses to identify trends and patterns in data quality issues, proactively addressing risks and proposing solutions; prepare and maintain detailed reports, documentation, and feedback materials for internal and client use.
- collaborate with project coordinators to streamline QA operations and reduce redundancies; stay updated on the latest tools, protocols, and industry advancements; and provide critical feedback on platform features and processes to ensure alignment with client requirements and overall operational efficiency.