Product Data Scientist

Responsibilities: Usage measurement and insights. Define what engagement means for a professional engineering tool, build the metrics that capture it, publish them where the teams that need them will see them, and keep them trustworthy as the product changes. Product insight. Pose and answer questions about feature adoption, workflow patterns, and friction that will inform product strategy and feature decisions. Partner with product management on the questions they bring, and originate your own. Engineering support. Help engineers identify and debug workflows that are failing, monitor how usage scales across customers and features, and surface where the product can be improved. Usage to business. Join application behavior with account and revenue data to find where usage predicts expansion, churn, or an unmet need, and turn that into recommendations the sales and product teams can act on. Model usefulness. Measure whether users find the model's output useful in their workflow. The AI team owns how good the model is. You own whether users are getting value from it. Getting the data. Build and maintain the pipelines that turn source databases and unstructured data into analysis-ready data sets while protecting customer confidentiality. You can think about high-level analysis but also translate that into and build the pipelines needed to connect the databases, do the analysis and present results to executives. The measure of the role is how often product and go-to-market decisions at Vinci are made with evidence that you produced, and how often that evidence changed a decision.