AI Data Quality Analyst

.The People First culture at Task Us has enabled the company to expand its workforce to approximately 45,000 employees globally. Presently, we have a presence in twenty-three locations across twelve countries, which include the Philippines, India, and the United States.

It started with one ridiculously good idea to create a different breed of Business Processing Outsourcing (BPO)! We at Task Us understand that achieving growth for our partners requires a culture of constant motion, exploring new technologies, being ready to handle any challenge at a moment’s notice, and mastering consistency in an ever-changing world.
** 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.
** 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 - Analyze 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 analyze 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.
** Experiences you’ll bring:
*** 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.
** Core skills you'll need:
*** 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.
*** Nice to have:
**** 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…