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Own end-to-end product-led growth (PLG) for Prolific's self-serve B2B SaaS customers: drive acquisition, activation, engagement and retention across paid and organic channels, build onboarding and lifecycle programs, define PQLs, optimize paid strategy, partner with Product/Sales/RevOps/CS, and run experimentation to improve funnel metrics.

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.

Define and build quality frameworks for human data programs, partner with clients and engineering to design evaluation, recruitment and annotation strategies, implement automated checks and dashboards, investigate data integrity, and upskill teams to deliver high-quality human data at scale.

The Data Quality Engineer ensures the quality and integrity of data in studies, developing measurement systems, automation, and conducting analysis to uphold standards across projects.