Data Quality Analyst

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:

• 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.

• Pinpoint and resolve annotation issues: Analyse error patterns, edge-case annotations, and root causes to surface insights and elevate annotation quality.

• Optimise annotation processes: Become proficient in annotation and QA tools, propose automation, and maintain clear documentation of quality standards, guidelines, and procedures.

• Collaborate and coach: Work closely with stakeholders and annotators to clarify requirements, provide constructive feedback, and support skill development within the annotation pipeline.

• Track and communicate quality performance: Regularly report on data quality metrics, key findings, and actionable insights to drive continuous improvements across projects.

Data Quality Analyst Responsibilities:

Data Analysis

• Quality Audits
• Perform quality audits on annotated datasets to ensure that they meet established guidelines and quality benchmarks.
• Statistical Reporting
• Leverage statistical based quality metrics such as F1 score and inter-annotator agreement to evaluate data quality.
• Root Cause Analysis
• Analyse annotation errors, trends, project processes, and project documentation to identify and understand the root cause of errors and propose remediation strategies.
• Edge-Case Management
• Resolve and analyse edge-case annotations to ensure quality and identify areas for improvement.
• Tooling
• Become proficient in using annotation and quality control tools to perform reviews and track quality metrics.
• Guidelines
• Become an expert in the project specific guidelines and provide feedback for potential clarifications or improvements.

Continuous Improvement

• Automation
• Identify opportunities to use automation to help enhance analytics, provide deeper insights, and improve efficiency.
• Documentation
• Develop and maintain up-to-date documentation on quality standards, annotation guidelines, and quality control procedures.
• Feedback
• Provide regular feedback that identifies areas for improvement across the annotation pipeline.

Collaboration & Communication

• Cross-Functional Teamwork
• Work closely with key project stakeholders and clients to understand project requirements and improve annotation pipelines.
• Training
• Assist with training annotators, providing guidance, feedback, and support to ensure data quality.
• Reporting
• Provide regular updates that highlight data quality metrics, key findings, and actionable insights for continuous process improvements.

Required Qualifications

• Bachelors degree in a technical field (e.g. Computer Science, Data Science) or equivalent practical experience.
• 1+ years of experience as a data analyst with exposure to data quality and/or data annotation - ideally within an AI/ML context.
• Familiarity with the basic concepts of AI/ML pipelines and data.
• Strong analytical and problem-solving skills with an exceptional eye for detail.
• Excellent written and verbal communication skills, with the ability to clearly articulate quality issues and collaborate with diverse teams.
• Ability to work independently and manage time effectively to meet deadlines.
• A strong problem-solver who thinks critically and drives innovation and continuous optimization.
• A quick learner with the ability to work independently in a fast-paced environment.
• A strong focus on detail, balanced against strategic priorities.
• A positive can-do attitude and the ability to easily adapt to new environments.
• Not afraid to speak up.

Preferred Qualifications

• Familiarity with data annotation tools (e.g. Labelbox, Dataloop, LabelStudio etc.).
• Experience working with multi-modal AI/ML datasets (images, videos, text, audio).
• Prior experience in an agile or fast-paced tech environment with exposure to AI/ML pipelines.
• Knowledge of programming languages (e.g. Python).
• Knowledge of the concepts and principles of data quality for AI/ML models and the impacts it can have on model performance.
• Working understanding of common quality metrics and statistical methods used in AI/ML data quality.
• Knowledge of AI/ML concepts and experience with data for AI/ML models.
• Experience in prompt engineering and leveraging LLMs in your day-to-day work.