Prolific
Data Analyst
Overview
As Prolific scales, maintaining a healthy and sustainable participant marketplace is critical to meeting the growing demand for high-quality human data. To champion this effort, you will own analytics across the entire participant lifecycle, spanning acquisition, onboarding, activation, engagement, quality, retention, and reactivation.
About Prolific
Prolific is not just another player in the AI space—we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.
Requirements & Eligibility
- Experience analyzing user acquisition, onboarding, activation, engagement, retention, churn, and behavioral cohorts.
- Advanced SQL and strong Python proficiency (e.g., pandas, experimentation analysis, automation), with a track record of building scalable reporting frameworks in modern BI tools (Metabase, Looker, Tableau).
- Experience designing and analyzing A/B or operational experiments, with solid grounding in statistical concepts and causal thinking.
- Ability to translate participant and marketplace challenges into measurable hypotheses, success metrics, and actionable recommendations.
- Strong ability to connect operational metrics to revenue, margin, and financial performance.
- Comfortable operating with high autonomy in a fast-moving environment, setting priorities, challenging assumptions, and driving initiatives.
- Proven ability to partner across Product, Operations, Commercial, and Finance functions.
- Strong grasp of B2B and two-sided marketplace metrics and economics.
Key Responsibilities
- Working hands-on with SQL and Python
- Building the metrics, dashboards, and experimentation frameworks that help us understand behavior and improve the participant experience.
- Partnering closely with Product, Operations, Growth, and Data Science
- Identifying opportunities, structuring experiments, and translating complex marketplace data into clear decisions that ensure participant health, marketplace liquidity, and long-term supply sustainability.
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