Team Lead, Human Data Operations - Post Training

Accountabilities

• Own end-to-end quality and delivery for assigned Human Data projects, personally reviewing domain-specific work and ensuring accuracy, consistency, guideline adherence, and high-quality data production at scale.


• Lead, coach, and performance-manage a team of AI Tutors through regular reviews, documented feedback, action plans, shadow sessions, and continuous development.


• Participate directly in labeling, annotation, and review activities to maintain quality standards and demonstrate best practices.


• Establish and maintain rigorous guideline adherence, taxonomy management, and quality assurance processes.


• Monitor team and project performance through KPIs such as quality scores, throughput, send-back rates, and other operational metrics.


• Identify operational bottlenecks and implement process improvements to increase efficiency, quality, and scalability.


• Create, maintain, and deliver training materials, practice exercises, and certification benchmark tasks.


• Manage certification processes and make workforce adjustments based on performance, project needs, and operational requirements.


• Collaborate with Human Data Managers, other Team Leads, and Engineering teams to translate model requirements into clear labeling strategies and operational guidelines.


• Develop team capabilities while maintaining strong accountability and a high-performance culture, including supporting disciplinary processes when necessary.


• Document project outcomes, identify opportunities for process iteration, and communicate project status, risks, and results to relevant stakeholders.


• Represent the needs and perspectives of AI Tutors while fostering effective collaboration across the wider Human Data organization.


• Balance strategic thinking with hands-on execution in a fast-moving environment where priorities may evolve quickly.

Requirements

• At least 1 year of hands-on experience in data labeling, annotation, AI training or evaluation, content quality, or a similar operational environment.


• Bachelor’s degree or at least 4 years of relevant professional experience in place of a degree.


• Demonstrated understanding of data quality metrics, annotation processes, and guideline-driven workflows.


• Basic understanding of artificial intelligence and machine learning concepts, particularly the relationship between training data quality and model performance.


• Familiarity with project management and collaboration platforms such as Notion, Axiom, JIRA, Linear, or equivalent tools.


• Previous experience leading or mentoring small teams in data labeling, annotation, content quality, or related operations is highly valued.


• Strong domain expertise in at least one relevant field, such as design, psychology, philosophy, writing, or another humanities discipline, is preferred.


• Proven ability to manage multiple concurrent projects and prioritize effectively in a fast-paced environment.


• Strong experience reviewing domain-specific work, maintaining data quality, and improving operational processes.


• Experience with coaching, performance management, training development, and certification programs is an advantage.


• Strong analytical capabilities and the ability to use data and KPIs to identify trends and drive continuous improvement; SQL knowledge is a plus.


• Experience managing distributed teams is desirable.


• Excellent written and verbal communication skills, with the ability to build rapport, communicate clearly, and align diverse stakeholders.


• Exceptional organizational skills and attention to detail.


• Proactive and structured approach to problem-solving, with a strong focus on continuous improvement.


• Ability to combine strategic thinking with hands-on execution.


• Strong interest in developing high-performing teams and scaling reliable, high-quality Human Data operations.


• Fluent English communication skills.


• Ability to work effectively in a highly collaborative, flat, and fast-moving environment where initiative and ownership are expected.

• Fully remote working opportunity, subject to applicable employment and location requirements.


• Competitive compensation, with international salary details shared during the recruitment process.


• Equity participation as part of the overall rewards package, where applicable.


• Comprehensive medical, dental, and vision coverage for eligible employees, depending on location and employment type.


• Access to retirement benefits such as a 401(k) plan for eligible U.S.-based positions.


• Short- and long-term disability insurance and life insurance for eligible employees.


• Paid sick leave and additional benefits depending on location and jurisdiction.


• Various employee discounts, perks, and additional rewards.


• Opportunity to work at the intersection of AI, human data, and advanced machine learning systems.


• Direct impact on the quality of data used to train and evaluate next-generation AI models.


• Opportunity to lead and develop a high-performing distributed team.


• Hands-on exposure to complex AI training and evaluation operations.


• Fast-paced environment with significant opportunities to contribute ideas, improve processes, and take ownership.


• International and collaborative working environment.

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