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2023 · ongoing·Data Scientist

Staffing forecasts the ops team actually uses

Replaced gut-feel staffing with a forecast that ops trusts — because they could see the why.

-31%
Forecast error
-18%
Overtime cost
100%
Adoption

Context

Weekly staffing was set by intuition, leading to costly over- and under-staffing. Past forecasting attempts failed because ops didn't trust black boxes.

Approach

  1. 01Co-designed the model interface with ops leads so outputs matched their mental model.
  2. 02Built an interpretable forecast with explicit seasonality and event adjustments.
  3. 03Shipped a what-if view so planners could test scenarios themselves.

Impact

Adoption hit 100% within a month — the explainability was the feature. Forecast error dropped 31% and overtime spend fell 18%.

ForecastingVisualizationStakeholders