A data scientist shares how Scrum, despite initial skepticism, became valuable for data science teams. The post covers four key Scrum elements adapted for data science: time-boxed iterations (to prevent rabbit-hole research and limit loss on failing bets), prioritization sessions with business stakeholders (to align data efforts with business goals), demos (to boost morale, accountability, and cross-team understanding), and retrospectives (to surface team feedback and drive continuous improvement). The author acknowledges Scrum's limitations in pure research settings but argues its benefits far outweigh the overhead in lean, product-focused environments.
