Principal, Product Analytics

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US.

As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations.

Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow.

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.

• 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 with unstructured data.

• 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.

• 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.