As AI-driven solutions expand across industries, ensuring the fidelity of training and evaluation datasets is essential for building reliable models. TaskUs needs a diligent, detail-focused Data Quality Analyst who can maintain annotation standards and drive continuous improvements so that essential AI data remains accurate, efficient, and scalable.
The impact youll make:
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Guard data integrity through audits:
Conduct quality audits on annotated datasets against established guidelines and statistical benchmarks (e.g., F1 score, interannotator agreement) to uphold data reliability.
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Optimise annotation processes:
Become proficient in annotation and QA tools, propose automation, and maintain clear documentation of quality standards, guidelines, and procedures.
Data Quality Analyst Responsibilities:
Data Analysis
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Quality Audits
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Perform quality audits on annotated datasets to ensure that they meet established guidelines and quality benchmarks.
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Statistical Reporting
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Leverage statistical based quality metrics such as F1 score and inter-annotator agreement to evaluate data quality.
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Root Cause Analysis
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Analyse annotation errors, trends, project processes, and project documentation to identify and understand the root cause of errors and propose remediation strategies.
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Edge-Case Management
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Resolve and analyse edge-case annotations to ensure quality and identify areas for improvement.
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Tooling
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Become proficient in using annotation and quality control tools to perform reviews and track quality metrics.
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Guidelines
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Become an expert in the project specific guidelines and provide feedback for potential clarifications or improvements.
Continuous Improvement
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Automation
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Identify opportunities to use automation to help enhance analytics, provide deeper insights, and improve efficiency.
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Documentation
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Develop and maintain up-to-date documentation on quality standards, annotation guidelines, and quality control procedures.
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Feedback
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Provide regular feedback that identifies areas for improvement across the annotation pipeline.
Collaboration & Communication
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Cross-Functional Teamwork
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Work closely with key project stakeholders and clients to understand project requirements and improve annotation pipelines.
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Training
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Assist with training annotators, providing guidance, feedback, and support to ensure data quality.
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Reporting
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Provide regular updates that highlight data quality metrics, key findings, and actionable insights for continuous process improvements.
Required Qualifications
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Bachelors degree in a technical field (e.g. Computer Science, Data Science) or equivalent practical experience.
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1+ years of experience as a data analyst with exposure to data quality and/or data annotation - ideally within an AI/ML context.
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Familiarity with the basic concepts of AI/ML pipelines and data.
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Strong analytical and problem-solving skills with an exceptional eye for detail.
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Excellent written and verbal communication skills, with the ability to clearly articulate quality issues and collaborate with diverse teams.
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Ability to work independently and manage time effectively to meet deadlines.
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A strong problem-solver who thinks critically and drives innovation and continuous optimization.
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A quick learner with the ability to work independently in a fast-paced environment.
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A strong focus on detail, balanced against strategic priorities.
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A positive can-do attitude and the ability to easily adapt to new environments.
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Not afraid to speak up.
Preferred Qualifications
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Familiarity with data annotation tools (e.g. Labelbox, Dataloop, LabelStudio etc.).
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Experience working with multi-modal AI/ML datasets (images, videos, text, audio).
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Prior experience in an agile or fast-paced tech environment with exposure to AI/ML pipelines.
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Knowledge of programming languages (e.g. Python).
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Knowledge of the concepts and principles of data quality for AI/ML models and the impacts it can have on model performance.
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Working understanding of common quality metrics and statistical methods used in AI/ML data quality.
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Knowledge of AI/ML concepts and experience with data for AI/ML models.
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Experience in prompt engineering and leveraging LLMs in your day-to-day work.
