Head of Product Analytics & Experimentation

The role You'll lead product analytics at qlub and build the experimentation engine behind our growth, scaling to 50+ A/B tests a month. You'll set the standard for how our data is modelled so analytics and AI can trust it, and lead a team of BI analysts working closely with Product, Operations, Tech and Data Engineering. What you ll own • Own experimentation end to end: configure and scale our existing A/B testing platform, set standards for test design, sample sizing, metrics and guardrails, and review results. • Train product managers and operations leads to run sound experiments themselves, and build the playbooks, templates and review rituals that make this routine. • Define the analytical data model: entity and event design that joins consistently across product flows, slowly changing dimensions, a semantic layer, and metric definitions. • Introduce data contracts and governance standards with Tech and Data Engineering, so that new events and tables are designed correctly from the start. • Coach and grow a team of three junior analysts and one senior analyst. Who you are • You've built and scaled an experimentation culture, and you're rigorous on test design while pragmatic about when evidence is enough to decide. • You love teaching, turning PMs and ops leads into confident experimenters and growing analysts through direct, kind coaching. • You think in systems and care about data models, definitions and governance, because reliable analytics and AI depend on them. • You drive change through influence and clear reasoning, partnering well across Product, Operations, Tech and Data Engineering. • You communicate clearly with executives and non-technical stakeholders, and you're excited to bring an AI-first approach to analytics in a fast-moving, global fintech. What you ll need • 5+ years in analytics or data science, including time at a large data-driven company (e.g. Delivery Hero, Uber, Amazon, Meta, Booking) or a modern data or experimentation platform (e.g. Sigma, Mixpanel, Amplitude, Statsig, Optimizely). • Hands-on experience building or scaling an experimentation programme, with solid statistics: power analysis, variance reduction, sequential testing, and common pitfalls. • Strong data modelling: dimensional modelling, SCDs, event schema design, dbt, and SQL at a high level. • A track record of driving change: introducing new practices into organisations and getting non-technical teams to adopt them. • Clear communication with executives and with non-technical stakeholders. Nice to have • Data science and predictive modelling, including forecasting. • Experience building an AI-first data stack and taking an AI-first approach to analytical problems. • Data governance frameworks, marketing analytics and attribution. • Experience with tools and technologies such as dbt, Airflow, Git, Google Cloud Storage (GCS), BigQuery, or ClickHouse. • Familiarity with data governance and cataloguing tools such as Atlan, Alation, or OpenMetadata. Who you are • You've built and scaled an experimentation culture, and you're rigorous on test design while pragmatic about when evidence is enough to decide. • You love teaching, turning PMs and ops leads into confident experimenters and growing analysts through direct, kind coaching. • You think in systems and care about data models, definitions and governance, because reliable analytics and AI depend on them. • You drive change through influence and clear reasoning, partnering well across Product, Operations, Tech and Data Engineering. • You communicate clearly with executives and non-technical stakeholders, and you're excited to bring an AI-first approach to analytics in a fast-moving, global fintech. What you ll need • 5+ years in analytics or data science, including time at a large data-driven company (e.g. Delivery Hero, Uber, Amazon, Meta, Booking) or a modern data or experimentation platform (e.g. Sigma, Mixpanel, Amplitude, Statsig, Optimizely). • Hands-on experience building or scaling an experimentation programme, with solid statistics: power analysis, variance reduction, sequential testing, and common pitfalls. • Strong data modelling: dimensional modelling, SCDs, event schema design, dbt, and SQL at a high level. • A track record of driving change: introducing new practices into organisations and getting non-technical teams to adopt them. • Clear communication with executives and with non-technical stakeholders. Nice to have • Data science and predictive modelling, including forecasting. • Experience building an AI-first data stack and taking an AI-first approach to analytical problems. • Data governance frameworks, marketing analytics and attribution. • Experience with tools and technologies such as dbt, Airflow, Git, Google Cloud Storage (GCS), BigQuery, or ClickHouse. • Familiarity with data governance and cataloguing tools such as Atlan, Alation, or OpenMetadata.