synthesia - Principal, Product Analytics
Requirements
• Deep product analytics experience across growth and broader product areas, with a record of investigations that changed decisions. • Data science and modelling experience, and the ability to explain how you applied it and what business impact it had. • Strong grounding in correlation, regression and causal analysis. • A proactive approach. You surface insights and shape the agenda rather than working to a brief. • Genuine curiosity. You notice what looks off and dig until you understand why. • Clear communication with management and executives about business impact, simplifying complex analysis into actionable recommendations. • Experience at an AI-native company, or with the added data complexity AI products bring. • Understanding of subscription businesses. • Understanding of the PLG motions in a B2B space. • Worked on multi-product platforms where upsells, cross-sells and bundling were a key revenue driver. • Worked alongside qualitative research, connecting the what (data) with the why (qual). • Done pricing and packaging work: willingness-to-pay, price testing, bundle performance. • We optimise for responsibility and freedom. • No ticket conveyor belt. We run on ownership and a small number of high-impact projects. • Close collaboration with Product, Engineering, Growth and commercial teams to ship real outcomes. • A bias toward pragmatic solutions that get used, measured and improved.
Responsibilities
• 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.
Benefits
• Do the hardest, most interesting analytical work in the company, with direct line of sight to product and strategy decisions. • Work across structured and unstructured data, customer conversations and AI evaluation, at the intersection of product usage, AI and revenue. • Independence and impact at principal level, without the management overhead.
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