<p>The Data Labelling Operations Manager will lead and scale annotation teams to deliver high quality labelled data for machine learning projects. This role balances people management, process design and quality assurance to meet strict delivery SLAs and regulatory requirements.</p>
<p>This job description outlines the role, duties and required skills for hiring a Data Labelling Operations Manager who will ensure accurate, timely and compliant data annotation at scale.</p>
<h2>Data Labelling Operations Manager Job Profile</h2>
<p>The Data Labelling Operations Manager manages end to end labelling operations across internal teams and external vendors. They define standards, implement tooling and monitor quality metrics to support data science and machine learning programmes.</p>
<p>Reporting to the Head of Data or Operations, the role combines operational leadership, workforce planning and continuous improvement to drive annotation throughput and reduce error rates.</p>
<h2>Data Labelling Operations Manager Job Description</h2>
<p>The Data Labelling Operations Manager is responsible for designing and operating processes for the creation, validation and delivery of labelled datasets. They develop clear annotation guidelines, establish quality assurance frameworks and introduce metrics to track accuracy, consistency and productivity.</p>
<p>Working closely with data scientists, product and engineering teams, the manager translates model requirements into labelling specifications and ensures the annotation toolchain is fit for purpose. They coordinate recruitment, training and day to day task allocation for annotators, team leads and QA staff while managing relationships with third party vendors as required.</p>
<p>The role requires strong analytical skills to interpret quality trends, root cause labelling errors and implement corrective actions. The manager also owns capacity planning, resource forecasting and budget oversight for annotation activities, and ensures processes comply with data protection and ethical guidelines.</p>
<h2>Data Labelling Operations Manager Duties and Responsibilities</h2>
<ul>
<li>Lead and mentor annotation teams, team leads and quality assurance personnel.</li>
<li>Define annotation standards, guidelines and acceptance criteria for datasets.</li>
<li>Design and implement QA processes, sampling strategies and error tracking.</li>
<li>Set and monitor KPIs such as accuracy, throughput, turnaround time and cost per item.</li>
<li>Optimise annotation workflows and introduce automation where appropriate.</li>
<li>Manage vendor selection, contracts and performance for outsourced labelling.</li>
<li>Coordinate recruitment, onboarding and ongoing training programmes for annotators.</li>
<li>Partner with data scientists and engineers to refine labelling taxonomies and tooling requirements.</li>
<li>Conduct root cause analysis on labelling issues and implement corrective plans.</li>
<li>Ensure compliance with data protection, privacy and ethical standards in annotation tasks.</li>
<li>Prepare regular operational and quality reports for senior stakeholders.</li>
<li>Manage budgets, forecasts and resource allocation for labelling operations.</li>
</ul>
<h2>Data Labelling Operations Manager Requirements and Qualifications</h2>
<ul>
<li>Bachelor's degree in Computer Science, Data Science, Operations Management or related discipline.</li>
<li>Proven experience managing annotation or data operations teams in an AI or ML environment.</li>
<li>Strong understanding of annotation tools, data pipelines and common labelling tasks across modalities.</li>
<li>Experience with quality assurance methodologies, statistical sampling and inter annotator agreement metrics.</li>
<li>Excellent people management skills and experience with hiring, coaching and performance reviews.</li>
<li>Analytical mindset with the ability to interpret metrics and drive process improvements.</li>
<li>Familiarity with data protection legislation and best practice for handling sensitive information.</li>
<li>Experience managing external vendors and negotiating SLAs and contracts.</li>
<li>Comfortable working with cross functional teams including data science, engineering and product.</li>
<li>Strong written and verbal communication skills in English; attention to detail is essential.</li>
</ul>