A Meridian business analyst with strong spreadsheet and dashboarding skills wants to help with the demand-forecasting project but has no formal machine-learning training. How should the PM appropriately involve them?

Distinguishing the roles of data scientists and citizen data scientists is an explicit Business Understanding enabler. A citizen data scientist is a role for someone with strong analytical skills and domain knowledge but without deep formal ML training — appropriately involved through accessible tools such as dashboards, AutoML, and exploratory analysis that let them contribute meaningfully without being handed core model-development responsibilities that require deeper technical expertise. This scenario's business analyst fits that role well, and pairing them with a trained data scientist for core development gets the benefit of both domain familiarity and technical rigor. The choice that assigns them to independently design the neural-network architecture gives them a task that requires exactly the formal ML training this analyst lacks, risking a poorly designed model. The choice that excludes them entirely wastes a genuinely useful contributor and ignores CPMAI's recognition of the citizen-data-scientist role as legitimate and valuable. The choice that has them independently validate the model's statistical significance before release similarly misassigns a technically rigorous validation task to someone without the statistical background to perform it reliably. The correct answer matches the person's actual skill set to an appropriate, well-scoped contribution.