What does a Data Quality Analyst do? As a Data Quality Analyst, you will be responsible for ensuring the accuracy, consistency, and overall quality of annotated datasets. Your role is critical in maintaining data integrity by conducting rigorous quality audits and leveraging statistical metrics such as F1 scores and inter-annotator agreement to assess performance. You'll dive deep into annotation errors and project trends to conduct root cause analyses, propose data driven solutions, and support long-term process improvements. You'll review and resolve complex edge cases, identify gaps in annotation logic, and recommend enhancements that directly impact quality outcomes.

As a tool expert, you'll work hands-on with annotation and quality control platforms, tracking quality metrics and identifying patterns that inform your evaluations. You'll master project-specific guidelines and contribute to their refinement, ensuring alignment with evolving data requirements. With a mindset for continuous improvement, you'll explore opportunities to introduce automation to streamline workflows and enhance insights. You'll also maintain comprehensive documentation on quality procedures, annotation standards, and evaluation protocols to support transparency and consistency across the team.

In collaboration with stakeholders, you'll align quality assurance processes with project goals. You'll assist in training new and existing annotators, providing clear guidance and timely feedback to uphold excellence. Finally, you'll deliver detailed reports that summarize data quality trends, identify key insights, and offer actionable recommendations to improve overall performance.

Do you have what it takes to become a Data Quality Analyst?

Requirements:

What we're looking for:

• A Bachelor's degree in a technical field such as Computer Science, Data Science, or a related discipline-or equivalent hands-on experience.
• At least 1 year of experience working in data analysis, data quality, or data annotation-preferably within AI/ML projects.
• A solid grasp of AI/ML pipeline fundamentals and how data quality impacts model performance.
• Sharp analytical and problem-solving skills with a laser-sharp attention to detail.
• Strong written and verbal communication skills-able to explain complex issues clearly and collaborate effectively across teams.
• Highly organized with the ability to work independently and manage multiple tasks under tight deadlines.
• A critical thinker who's always looking to innovate, optimize, and elevate the way we work.
• Quick to learn, quick to adapt, and thrives in fast-paced environments.
• Strategic, but with a hands-on approach and a keen eye for both the big picture and the finer details.
• A team player with a positive attitude who isn't afraid to challenge the status quo and speak up.

Bonus points if you have:

• Experience with data annotation platforms like Labelbox, Dataloop, or LabelStudio.
• Familiarity working with multi-modal datasets-text, image, video, or audio.
• A background in tech environments that move fast and embrace agility, especially those with AI/ML exposure.
• A working knowledge of Python or similar programming languages.
• An understanding of data quality frameworks and their influence on AI/ML outcomes.
• Experience applying quality metrics and statistical analysis to AI/ML data workflows.
• Hands-on experience with LLMs and prompt engineering in real-world tasks.