Data Scientist | Modelling & MMM
Summary
Data Scientist at Wise Pirates in Porto building predictive and causal models — demand forecasting, customer clustering, churn/LTV, propensity, uplift, incrementality and marketing mix modelling — to guide media plans and business decisions. Success is judged on revenue, retention and efficiency impact, with a strong culture of sharing knowledge across the data team.
Inner Data is a collaborative area, with many people sharing their methodologies and knowledge, so you should be ready to share your ideas and solutions with your team.
This seat is about what will happen and what caused it, which is a different discipline from measuring what did happen. Demand and revenue forecasting, customer clustering and cohorts, propensity, churn and lifetime value, recommendation and next best action, and the causal side: uplift models, incrementality, and marketing mix modelling when attribution has run out of road.
We care more about a model that changes a decision than about one that scores well. A forecast nobody plans against and an MMM that never reaches a media plan are both failures, whatever the error metric says. Expect to be judged on revenue, retention and efficiency, and to explain uncertainty honestly to people who would prefer a single number. Like everyone here, you will be judged on the marginal value you add to the company and to the projects you touch.
