Human Data Operations Strategist

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

Nice-to-haves

• 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