Head of Applied AI, Marketing Data Science
About The Position
We're empowering small teams with technology that makes it easier to market and grow businesses. Our current focus is to help consumer brands shift from "workflow automation" to "agent management" within their marketing operations. Shadow is the AI coordination layer — providing shared AI memory, centralized agent control, and model orchestration for marketing teams. Shadow is built alongside Darkroom — a performance marketing agency that's been operating for 10 years, employs 100+ people, runs 100+ clients at a time, and has worked with over 1,000 consumer brands. This role plugs directly into that knowledge and turns it into product. 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. You report directly to the CEO of Shadow. This is for someone who's spent years in the work and now wants to lean into the technology — leveraging hard-won marketing experience to build, not to manage accounts. This is not a client-facing role.
Requirements
- 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.
Nice To Haves
- Familiarity with modern warehouse/analytics stacks (BigQuery, dbt) — enough to design schemas and collaborate with eng.
- Agency or multi-brand background (pattern recognition across accounts).
- Built attribution models, forecasting/MMM, or internal analytics dashboards.
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.
Benefits
- Competitive salary (roles, responsibilities, and comp grow as we do)
- Top-tier health, vision, dental insurance (US)
- Regular team off-sites
- Regular hack weeks
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
