Forward-Deployed Marketer

Overview What We're Building 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. The role 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. What You\'ll Own 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. Must have 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 have 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. Culture fit You’re a power AI user. You've embedded AI into every workflow you touch and you think in systems — not one-off prompts, but repeatable structures that compound. Entrepreneurial. You don't need much direction to move fast, you pivot when the situation demands it, and what you ship is production-grade, not a prototype you hand off for someone else to finish. What we offer Competitive salary (roles, responsibilities, and comp grow as we do) Top-tier health, vision, dental insurance (US) Regular team off-sites Regular hack weeks Compensation Yearly compensation for this role is $180,000. Actual compensation will be determined based on experience, skills, and qualifications. This role is also eligible for performance-based compensation. A summary of benefits is listed above. Equal Opportunity Statement Darkroom is an equal opportunity workplace — we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. #J-18808-Ljbffr Darkroom