Product Analyst

Product Analyst

Analytics

  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.

We’re looking for a Product Analyst to help Prolific strategically scale our human data pool. Central to our mission of delivering high-quality data to cutting-edge AI developers, you’ll be responsible for analysing the product lifecycle of new feature releases on Prolific’s platform and increase metrics such as participant acquisition, engagement, and retention related to the pool. You’ll be a key part of the experimentation pipeline, using a mixture of ship-and-learn and A/B testing to measure feature performance and impact. The recommendations that you’ll make from the analyses offer an opportunity to shape Prolific’s long-term human data strategy and work at the heart of human data product innovation and AI research, fostering a metrics-driven and experimentation culture across the company.

What you’ll bring to the role

• Analytics Expertise: Basic proficiency in SQL for data extraction and manipulation, along with some experience using spreadsheets (Google Sheets, Excel). A willingness to learn Python for basic statistical analysis and dbt for building and reviewing data tables, as training and self-learning will be part of the role.
• Data Visualisation: Experience using a modern BI tool (e.g. Metabase, Looker, Tableau) to do table operations such as joins, filtering data and aggregations, and can choose appropriate visualizations to tell stories with data. 

• A/B Testing: Basic understanding of experimentation procedures and familiarity with some statistical concepts (e.g. Frequentist tests such as t-tests and chi-squared tests).
• User Analytics: An understanding of metrics important to Human Data Product, such as acquisition, engagement, and retention.
• Problem-Solving: Ability to analyze data to identify trends and business insights.
• Communication: Strong verbal and written communication to present findings effectively.
• Collaboration: Comfortable working with cross-functional teams (engineering, marketing, design).
• Attention to Detail: Ability to spot patterns and errors in data.
• Stakeholder Collaboration: A willingness to build partnerships with internal teams and stakeholders, confidently sharing metrics and actionable insights that drive meaningful results.
• Business Acumen & Data Storytelling: Adept at translating data into compelling narratives that influence decisions, combining a deep understanding of business priorities with impactful communication.
• Process Design & Documentation: A willingness to be a part of product roadmapping and helping direct the process product experimental pipeline, as well as maintaining clear, accurate documentation on feature release future plans and past findings.

What you’ll be doing in the role 

• Data Analysis & Insights: Collect and analyse product usage data to identify trends and opportunities. Strengthen and evolve key metrics to measure and optimise the performance of our human data pool, and conduct cohort analysis of our human data pool to understand customer behavior over time.
• A/B Testing & Experimentation: Assist in designing, running, and analyzing A/B tests to measure feature impact. Interpret test results to provide actionable recommendations and work with other analysts to refine testing methodologies.
• Reporting & Dashboarding: Develop and maintain dashboards in Metabase, sharing these with stakeholders and creating reports from them to support product and business decisions. 
• Self-service Solutions: Design dashboards and tools to empower teams to independently access and act on insights.
• Quality Monitoring: Develop and monitor metrics for participant quality, task completion rates, and data reliability to ensure high standards for AI training and evaluation tasks.
• Cross-Functional Collaboration: Work with Product Managers to define success metrics for new features, engineers to ensure accurate product tracking and data collection.
• Analytics Infrastructure: Partner with other analysts and analytics engineering to ensure aligned metrics definitions and effi