Demand and audience forecasting for financial and media clients (Artefact)
Two enterprise consulting engagements in parallel: a media group requiring audience reach forecasting for advertising inventory planning (forward-looking impressions and CPM estimation by placement type), and a financial services client requiring 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, 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: 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.
Enabled both clients to shift from reactive to predictive planning -- media client could commit advertising inventory with forward-looking reach estimates, financial client could optimize liquidity buffers against probabilistic transaction volume bands. Both engagements delivered on consulting timelines with internal client team handoff.