Principal, Product Analytics

Description

The company is an AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, it is headquartered in London and has offices and teams across Europe and the US.

AI is changing how people work, and the company builds products that improve visual communication and enterprise skill development, helping people perform better inside the organisations they work for.

The company recently closed a $200 million Series E at a $4 billion valuation, bringing total funding above $530 million. Investors include Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV and Evantic Capital, along with founders and operators from Stripe, Datadog, Miro and Webflow.

The company is hiring a Principal, Product Analytics to dig into why users and customers act as they do, then turn those findings into product and business decisions.

The role sits at the centre of a new, more proactive data function. You will take unclear, high-stakes questions about user behaviour and customer experience and follow the data wherever it goes, across acquisition, activation, usage and the wider customer journey.

You will work hands-on and largely on your own, with Product Managers, engineering managers and commercial teams. A VP of Growth will set the function's direction and build the team. Your findings will shape experiments, the roadmap and company strategy.

Responsibilities

• Look for problems nobody has asked about. Notice when a funnel, cohort or metric looks wrong, investigate it, and surface a product issue, a cost leak or an unexpected opportunity. You largely choose your own priorities and start digging without waiting to be asked.
• Lead in-depth investigations into user behaviour and customer experience across product and non-product touchpoints, including support, customer success and sales.
• Locate where activation fails and map the failure points in the user journey, including any that could become product features.
• Study acquisition channels, use cases and what users are trying to accomplish.
• Apply rigorous causal and statistical methods, including regression, causal analysis and well-designed experiments. Be explicit about what the evidence does and does not show.
• Build data science models for real business problems, such as churn models that trigger product actions in real time. Explain how each model is used and what it changes.
• Analyse unstructured and AI-specific data, including agent-user conversations, context layers and evaluation systems, together with structured product data.
• Estimate market opportunity sizes to help the business set priorities.
• Work with engineering managers on metrics that track organisational productivity.
• Turn findings into clear recommendations for product teams, managers and executives.

Requirements

• Deep product analytics experience across growth and wider product areas, with a track record of investigations that changed decisions.
• Experience with data science and modelling, and the ability to explain how you applied it and what business impact it delivered.
• Solid grounding in correlation, regression and causal analysis.
• A proactive approach. You surface insights and set your own agenda rather than waiting for a brief.
• Real curiosity. You notice what looks wrong and keep digging until you understand the cause.
• Ability to explain business impact to managers and executives and reduce complex analysis to actionable recommendations.
• Preferred: experience with unstructured data.
• Preferred: experience at an AI-native company or with the added data complexity that AI products bring.
• Preferred: understanding of subscription businesses.
• Preferred: understanding of product-led growth motions in B2B.
• Bonus: work on multi-product platforms where upsells, cross-sells and bundling drove a major share of revenue.
• Bonus: experience working alongside qualitative research to connect what the data shows with why users behave that way.
• Bonus: pricing and packaging work, including willingness-to-pay analysis, price testing and bundle performance.

Conditions

The team values responsibility and freedom.

You will own a small number of high-impact projects rather than a stream of tickets.

You will work closely with Product, Engineering, Growth and commercial teams to deliver outcomes that reach users.

The team favours pragmatic solutions that are used, measured and then improved.

Benefits

• Work on the most demanding and interesting analytical problems in the company, with a direct link to product and strategy decisions.
• Work across structured and unstructured data, customer conversations and AI evaluation, where product usage, AI and revenue meet.
• Independence and impact at principal level, without management responsibilities.