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.

Ran growth at one or more high-growth DTC / omni-channel consumer brands — you've managed paid media tactically, not just supervised people who did.
Fluency across the full marketing mix (Meta + Google, plus TikTok, email/SMS, marketplace, organic) — you think in MER/CM/LTV, not platform ROAS.
Real data science chops: SQL + Python/notebooks, statistical reasoning, building and validating metric models against messy real-world data.
Ability to translate between marketer intuition and rigorous structure — and a strong opinion about which metrics actually matter.

Head of Applied AI, Marketing Data Science

Key skills