Turn marketing strategies into data models and metrics for Shadow's AI.
•Part senior growth marketer, part data scientist, part applied-AI builder
•you turn the way elite marketers think into the data models, metrics, and schemas that power Shadow's intelligence layer.
•The majority of your time (roughly 75%) is spent on the product: designing the metric logic, data schemas, and analytical models Shadow's agents reason with, and working directly with marketing teams to translate what they actually do into structure the product can act on.
•Key Responsibilities Design the analytical models and metric logic the agent reasons with
•contribution margin (CM3), acquisition truth (aMER, NCAC), cohort LTV/payback, ad spend efficiency and marginal-return analysis, incrementality testing (geo lifts, conversion-lift, MMM calibration)
•from raw platform data to decision-ready insight.
•Define the schemas that encode marketing tradecraft: how creative, channel, financial, and customer data connect into a queryable picture of a brand.
•Own accuracy and judgment
•what's load-bearing vs. noise, where attribution lies, how to compute metrics that survive operator scrutiny.
•Spec the model; partner with data eng to build the pipeline and the AI team to wire it into agent skills.
•Guide incrementality testing strategy across Darkroom's Requirements Not specified