A data analyst on a supply team usually turns messy operational data into clear metrics, dashboards and experiments that explain how participants behave and where the experience breaks down. A strong candidate brings solid SQL and a visualization tool, comfort with experiment design and basic statistics, and the habit of checking data quality before trusting a number. Because the company runs a verified participant platform for researchers and AI labs, care with data integrity and honest reporting matters more than flashy charts.
How it fits the AI era
AI speeds up query writing, dashboard drafts and first pass analysis, so the analyst spends less time on mechanics and more on framing the right question and judging whether results make sense. The human parts stay central: deciding what to measure, spotting when data is wrong or gamed, and explaining findings to people who will act on them. On a platform built on real human responses, that judgment is the product.
How it builds AI experience
The role offers hands on practice using AI assistants for code and analysis while working with data about human participants, which builds a feel for where automated output can be trusted. A candidate can prepare by using AI tools on real datasets, then documenting where the tool was wrong and how it was caught. Reading up on data quality and fraud detection in online research also helps.
Worth showing
This is our own read, written from the employer’s posting and the company’s Spotlight. The employer’s posting is the source of truth for the role.
About Prolific
A platform of ID-verified human participants that researchers and AI labs use to collect real human feedback, at a time when AI-generated fake responses are becoming a growing problem in online research.
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