Responsibilities

Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams
Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops
Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process
Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices
Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies

Requirements

3–7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies
Proven ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and iteration
Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued
Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally in a context involving human-in-the-loop workflows or structured labelling tasks
Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions

Hands-on experience with computer vision, generative AI, or multimodal data workflows
Prior exposure to data annotation platforms or quality management frameworks
Experience coaching or managing operational teams