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Machine learning in a young start up does not look like a Kaggle competition. Data science projects start with a more extensive roadmap than dataset. In the absence of data, subject-matter knowledge makes heuristic solutions a tempting first step for all stakeholders involved. While rules-based algorithms are not the glamorous side of data science, they need not be a dead end and can form the basis for increasingly sophisticated labeled data. In this talk, we propose a path to iteratively bootstrap a supervised machine learning model out of heuristics and demonstrate its potential on the basis of N26 projects.