Principal, Product Analytics

We're looking for a Principal, Product Analytics who loves getting to the bottom of things. You'll do the deep investigative and technical work that explains why users and customers behave the way they do, and turn it into decisions that change the product and the business.

We're building a more proactive data function, and this role is its deep-investigation engine. You'll take ambiguous, high-stakes questions about user behaviour and customer experience and follow them wherever the data leads, across acquisition, activation, usage and the wider customer journey.

You'll work hands-on and independently, partnering with Product Managers, engineering managers and commercial teams, and working alongside a VP of Growth who will set the function's direction and build the team. Your findings will inform experiments, the roadmap and company strategy.

• Go looking for what nobody asked about. This is the heart of the role. You'll notice when something looks off in a funnel, a cohort or a metric, disappear into it, and come back with a product issue, a cost leak or an opportunity nobody was looking for. You set much of your own agenda, and you don't wait for a request to start digging.
• Lead deep investigations into user behaviour and customer experience across product and non-product touchpoints, including support, customer success and sales.
• Find where activation breaks. Identify failure points in the user journey, including those that could become productised capabilities.
• Understand acquisition and intent. Dig into acquisition channels, user use cases and what people are actually trying to achieve.
• Do rigorous causal and statistical work. Go beyond correlation with regression, causal analysis and well-designed experiments, and be clear about what the evidence does and doesn't support.
• Build models that drive action. Apply data science to real business problems, such as churn modelling that can trigger real-time product action, and explain clearly how models are applied and what impact they have.
• Analyse unstructured and AI-specific data. Work with agent-user conversations, context layers and evaluation systems alongside structured product data.
• Size opportunities. Produce market opportunity sizing that helps the business prioritise.
• Measure how we build. Partner with engineering managers on organisational productivity metrics.
• Communicate for impact. Translate findings into clear recommendations for product teams, management and executives.

…