AI Data Quality

About TaskUs: TaskUs 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, TaskUs serves clients in the fastest-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, FinTech, and HealthTech. The People First culture at TaskUs 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 TaskUs 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. What We Offer: At TaskUs, 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 TaskUs. Join our team today and experience firsthand our dedication to supporting People First. What can you expect in an AI Data Quality & Engineering Lead role with TaskUs: Why this role exists: As AI systems scale rapidly across industries, the integrity and accuracy of testing, training, and evaluation data have never been more critical. TaskUs needs a proactive leader who can architect and uphold high‑quality annotation workflows so that AI models are built and evaluated on reliable data without compromising on speed or efficiency. The impact you’ll make: Build and guide a high-performing team : Lead and mentor a team of Data Quality Analysts, setting clear quality goals, delivering feedback, and fostering a culture of precision and accountability. Ensure quality at scale : Develop and continually refine robust QA processes, SOPs, and statistical quality metrics (e.g., F1 score, inter‑annotator agreement) to protect the integrity of annotation outputs. Drive transparency and insight : Create dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities - communicating these insights to leadership and clients. Champion tool innovation and efficiency : Manage annotation and QA platforms (like Labelbox, Dataloop, LabelStudio), and lead the evaluation or implementation of new automation tools to elevate efficiency and maintain quality. Responsibilities: - Strategic Leadership Drive the development, refinement, and documentation of quality assurance processes and standard operating procedures to ensure high-quality outputs. Establish comprehensive quality metrics (e.g. F1 score, inter-annotator agreement) that align with business objectives and industry standards. Continuously review and refine annotation workflows to proactively identify risks and areas to increase efficiency and reduce errors. Act as the subject matter expert on annotation quality, providing ongoing feedback, training, and support to annotators and project teams to uphold the highest quality standards. - Analysis & Reporting Lead in-depth data analysis to diagnose quality issues, assess the effectiveness of quality strategies, and uncover root causes of recurring errors. Develop and maintain dashboards that provide real-time insights into quality metrics and project performance. Prepare and deliver strategic quality reports to senior management and clients, articulating quality trends, risks, and improvement plans. Partner with cross-functional teams, including operational management, engineering, and client services, to align on project goals and quality assurance initiatives. - Operational Leadership Lead a team of Data Quality Analysts and provide mentorship, training, and expertise, fostering a culture of continuous improvement and accountability. Manage the configuration and integration of annotation and quality control tools (e.g. Labelbox, Dataloop, LabelStudio), ensuring optimal tool performance and alignment with project requirements Identify, evaluate, and implement innovative quality control tools and automation technologies to streamline quality control workflows, enhance analytical capabilities, and improve operational efficiency. Required Qualifications Bachelor’s degree in a technical field (e.g. Computer Science, Data Science) or equivalent professional experience. 3+ years of experience in data quality management, data operations, or related roles within AI/ML or data annotation environments. Proven track record in designing and executing quality assurance strategies for large-scale, multi-modal data annotation projects. Proven track record in a leadership role managing and developing high-performing, remote or distributed teams. Deep understanding of data annotation processes, quality assurance methodologies, and statistical quality metrics (e.g., F1 score, inter-annotator agreement). Strong data-analysis skills, with the ability to interrogate large datasets, perform statistical analyses, and translate findings into actionable recommendations. Excellent communication skills, with experience presenting complex data and quality insights to technical and non-technical stakeholders. Proficiency with annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio). High-level of proficiency in common data-analysis tools, such as Excel and Google Sheets. Familiarity with programmatic data analysis techniques (e.g. Python, SQL). Familiarity with the core concepts of AI/ML pipelines, including data preparation, model training, and evaluation. Preferred Qualifications Prior experience in an agile or fast-paced tech environment with exposure to AI/ML pipelines. Experience in a managed services or vendor-driven environment. Familiarity with prompt engineering and large-language-model assisted workflows to optimise annotation and validation processes. In-depth knowledge of ethical AI practices and compliance frameworks. How We Partner To Protect You: TaskUs will neither solicit money from you during your application process nor require any form of payment in order to proceed with your application. Kindly ensure that you are always in communication with only authorized recruiters of TaskUs. DEI: In TaskUs we believe that innovation and higher performance are brought by people from all walks of life. We welcome applicants of different backgrounds, demographics, and circumstances. Inclusive and equitable practices are our responsibility as a business. TaskUs is committed to providing equal access to opportunities. If you need reasonable accommodations in any part of the hiring process, please let us know. We invite you to explore all TaskUs career opportunities and apply through the provided URL . #J-18808-Ljbffr