Project Specialist

Role Purpose

Appen's GenAI Project Delivery team sits at the intersection of AI development and real-world data quality, executing the annotation, evaluation, and data collection work that shapes how frontier models perform. The purpose of this role is to drive end-to-end delivery excellence across GenAI projects by ensuring task configuration, contributor performance, and data quality outcomes stay tightly aligned to client expectations. This is a hands-on, cross-functional role with influence over delivery standards, client outcomes, and the continuous improvement of how Appen executes at scale.

Your Impact

• Coordinate assigned project workstreams - covering productivity, qualification, training, crowd communications, and QA - to ensure on-time delivery against execution standards
• Deliver high-quality annotation and QA during POC and pilot phases to establish quality benchmarks and de-risk new client relationships from day one
• Partner with Client Partners and Pilot Teams to validate quote assumptions, including time-per-task and quality requirements, before finalization
• Analyze datasets to surface patterns, insights, and optimization opportunities that inform automation strategy and drive thought leadership content for customers
• Support Project Managers and Client Partners as the AI expert point of contact across client meetings, workshops, QBRs, and status updates
• Maintain version control and change communication protocols to ensure contributors are informed of guideline updates prior to launch and throughout production
• Translate QA findings into targeted improvements to supporting materials, reducing recurring annotation errors and improving overall data quality
• Provide data-backed findings and consultancy insight to strengthen Appen's advisory value during critical early project phases and ongoing client relationships

What You Bring

• Proven experience coordinating or delivering data annotation, content evaluation, or AI training data projects in a structured delivery environment
• Strong understanding of data quality principles, QA methodologies, and annotation workflows within GenAI or adjacent AI domains
• Ability to create clear, accurate contributor-facing documentation including guidelines, FAQs, and cheatsheets under tight timelines
• Experience supporting pilot or POC phases, including validating operational assumptions and establishing quality benchmarks
• Demonstrated ability to analyse datasets, identify patterns, and translate findings into actionable insights or process improvements
• Confident communicator with experience engaging cross-functional stakeholders, including client-facing participation in meetings, workshops, or reporting cycles
• Disciplined approach to version control, update management, and change communication across distributed contributor teams
• Comfortable operating across multiple concurrent workstreams with a high degree of ownership and attention to delivery standards