Demand and audience forecasting for financial and media clients (Artefact)

Two enterprise consulting engagements ran in parallel. A media group required audience reach forecasting for advertising inventory planning (forward-looking impressions and CPM estimation by placement type), and a financial services client required transaction volume forecasting for treasury operations (liquidity management and capital allocation planning).

Consulting sprint cadence, 2-week cycles from kickoff to senior-stakeholder demo, is structurally incompatible with the iterative data quality investigation that forecasting accuracy requires. Both engagements surfaced data gaps mid-sprint, sparse historical coverage for specific media placement types and thin transaction records for niche instrument categories. More critically, both clients arrived with pre-formed intuitions about expected forecast ranges; any model output that violated those intuitions demanded immediate explainability, not a follow-up memo, requiring the data scientist to function simultaneously as technical lead, domain translator, and stakeholder manager under a hard clock.

Rapid iteration cycle (consulting cadence of a 2-week sprint to client demo) requiring simultaneous data quality triage, model selection, business constraint integration (client-specific revenue targets, regulatory reporting periods), and documentation for handoff to internal client teams. Model outputs translated into planning-ready business language, not statistical summaries, for senior stakeholder consumption.

Full documentation delivered: model card, feature dictionary, retraining protocol, and dashboard user guide. Client teams validated outputs against business intuition before sign-off, a critical acceptance criterion in consulting delivery that requires the data scientist to function as both technical lead and domain translator.