Teams at Prolific

Lead scoping, design, and delivery of complex human-data projects for AI clients. Own study design, protocols, rater onboarding and QA, pilot execution, and cross-functional coordination to scale repeatable workflows and document playbooks to improve operational readiness and automation.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

The role involves training AI models using accounting expertise, including evaluating performance and providing feedback on tasks like financial statement interpretation and tax analysis.