We support a chain of specialty retailers, and their promotion planning is still painfully manual. Every campaign has a forecast, but nobody trusts it once the campaign starts.
We need someone to improve our promotion forecasting workflow. You'll analyze historical promotion performance, build baseline demand estimates, separate promo uplift from seasonality, and help us create a repeatable planning model.
The output should be practical: a model, a validation report, and clear guidance for planners.
Requirements
Technical stack needed for this mission.
- Forecasting or demand planning experience
- Strong Python and SQL
- Comfortable with messy retail data and calendar effects