AI Data Quality Analyst

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**** About Task Us:
**** Task Us is a provider of outsourced digital services and next-generation customer experience to fast-growing technology companies, helping its clients represent, protect and grow their brands. Leveraging a cloud-based infrastructure, Task Us serves clients in the fastest-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, Fin Tech, and Health Tech. At Task Us, we prioritize our employees' well-being by offering competitive industry salaries and comprehensive benefits packages.

Our commitment to a People First culture is reflected in the various departments we have established, including Total Rewards, Wellness, HR, and Diversity. We take pride in our inclusive environment and positive impact on the community. Moreover, we actively encourage internal mobility and professional growth at all stages of an employee's career within Task Us. Join our team today and experience firsthand our dedication to supporting People First.
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* What We Offer:

****** AI Data Quality Analyst    
** Why this role exists:
**** As AI-driven solutions expand across industries, ensuring the fidelity of training and evaluation datasets is essential for building reliable models. Task Us 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.
* 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.
* 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.
* Familiarity with data annotation tools (e.g. Labelbox, Dataloop, Label Studio 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.
* ** Work remote:
** Work from anywhere you’re most productive!
* ** Every dataset is unique:
** Audit, analyze, and improve complex AI/ML datasets, no two projects are ever the same.
* ** Visible impact:
** Your quality checks, root cause analyses, and automation ideas will directly shape how AI models perform in real-world applications.
* ** Room to grow:
** Build deep expertise in AI data quality and scale your influence as our projects and capabilities expand.
* ** Mission-driven team:
** Work alongside analysts, engineers, and stakeholders who share a passion for creating accurate, reliable, and trustworthy AI.
* ** Guard data integrity through audits:
** Conduct quality audits on annotated datasets against established guidelines and statistical benchmarks (e.g., F1 score, inter‑annotator 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.to learn more about our…