Forward-Deployed Marketer

About the role

You will architect the analytical models and metric logic that marketing agents use to reason about brand performance, defining contribution margin (CM3), aMER, NCAC, cohort LTV, payback, and ad spend efficiency from platform data to decision-ready insight. You will design the schemas that bind creative, channel, financial, and customer data into a unified, queryable picture of a brand, ensuring that every data structure reflects real marketing tradeoffs. You will own accuracy and judgment across the stack, distinguishing load-bearing signals from noise and evaluating where attribution holds and where it breaks under operator scrutiny. You will spec production-ready models and collaborate closely with data engineering and the AI team to turn those specs into pipelines and agent skills that survive live traffic. You will translate nuanced marketer intuition into rigorous structures, building opinions about which metrics truly matter and how they should be computed and validated. You will work exclusively with production systems and challenger consumer brands, turning the way elite marketers think into durable intelligence rather than managing external client accounts. You will directly influence the product and engineering culture, shaping tools, processes, and standards alongside a team that ships code daily.

Key facts

What you'll do

• Architect analytical models and metric logic that agent reasoning depends on, including CM3, aMER, NCAC, cohort LTV, payback, and marginal-return analysis.
• Design schemas that encode marketing tradecraft, connecting creative, channel, financial, and customer data into queryable structures.
• Own accuracy and judgment, determining what is load-bearing versus noise in attribution and how metrics hold up under operator scrutiny.
• Spec models and partner with data engineering and AI teams to build data pipelines and wire model outputs into agent skills.
• Translate marketer intuition into rigorous structures, defining which metrics matter and how they should be computed, validated, and iterated on.
• Work with high-growth DTC and omni-channel consumer brands, turning their operational reality into resilient analytical foundations.
• Turn elite marketing thinking into production-grade intelligence layers rather than managing external client accounts.
• Influence engineering and product culture by shaping tools, processes, and standards that compound over time.
• Build and validate metric models against messy real-world data, ensuring insights survive real operator scrutiny.
• Drive incrementality testing through geo lifts, conversion lift, and MMM calibration to ground truth in channel efficiency.
• Define data quality and lineage so that dashboards and agent outputs remain trustworthy as systems evolve.
• Establish guardrails for model behavior, balancing statistical rigor with practical marketing constraints.
• Contribute to hiring and culture by codifying standards that attract builders who combine marketing and data depth.
• Operate as a power AI user, embedding repeatable structures into workflows so compounding advantages emerge across campaigns.

Requirements

• Ran growth at one or more high-growth DTC or omni-channel consumer brands, managing paid media tactically and not only supervising people who did.
• Fluent across the full marketing mix, including Meta, Google, TikTok, email/SMS, marketplace, and organic, thinking in terms of MER, CM, and LTV rather than platform ROAS alone.
• Possess real data science chops, including SQL and Python or notebooks, statistical reasoning, and the ability to build and validate metric models against messy real-world data.
• Demonstrate the ability to translate between marketer intuition and rigorous structure, with strong opinions on which metrics actually matter.
• Operate at a level of ownership where you ship production-grade work daily and influence key product and technical decisions without needing constant direction.
• Thrive in an environment where you work directly with challenger consumer brands and engage deeply with CMOs, CEOs, and VPs generating $80M to $500M in revenue.
• Align with a culture of power AI usage, embedding AI into every workflow and building systems that are repeatable and compound rather than one-off prompts.
• Exhibit an entrepreneurial mindset, moving fast with autonomy, pivoting when necessary, and shipping production-grade outputs instead of prototypes.

Nice to have

• Familiarity with modern warehouse and analytics stacks such as BigQuery and dbt, enough to design schemas and collaborate effectively with engineering.
• Background in agency environments or across multiple brands, bringing pattern recognition from diverse customer contexts.
• Experience building attribution models, forecasting or MMM systems, or internal analytics dashboards that have survived live use.

Practical notes

This role is full-time and based in New York.