Help with analyzing customer data such as: usage data, adoption trends, and health score to help identify patterns and potential risks Collaborate closely with Sales, Marketing, and Product teams on upsell initiatives, adoption metrics, and understanding our customer base better

I’m a PhD mathematics student, and for the past few months I’ve been building a product for SaaS and subscription businesses. The basic idea is simple: It continuously monitors what is happening across every customer account and tells you: * Who is becoming more likely to churn * Why their risk is changing * How much revenue is currently at risk * Which revenue is still realistically saveable * Which customers your team should focus on first I’m not trying to build another dashboard that shows obvious things you can already find in Stripe or your analytics tool. The system is designed to find deeper patterns. For example, a customer might still be logging in regularly, but they have slowly stopped using the features that originally gave them value. Another account might look healthy because total activity is high, but almost all of that activity now comes from one person. If that person leaves, the entire account could collapse. Or a company or User may have purchased several seats, but adoption never spread beyond the original user. These things are difficult to notice when you have hundreds or thousands of customers. I’ve spent a lot of time designing the mathematical “brain” behind the product so it can combine many different signals, compare how a customer is behaving now with how they behaved before, and identify patterns that may suggest growing risk. But the most important part is that it is not meant to be a generic churn tracker. The whole monitoring system is personalised to the product using it. Every SaaS product has a different definition of healthy usage. For one product, creating a report might be the most important action. For another, it could be inviting team members, completing a workflow, using a specific feature repeatedly, connecting an integration, or reaching a certain level of collaboration....

Develop and own data models for product usage and adoption, giving CS, Support, and leadership clear visibility into how customers engage with the platform. Conduct ad-hoc, deep-dive data analysis for CS leadership, Sales, and Product, translating findings into clear, actionable recommendations.