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.
