AI Search & Web Lead
Own end-to-end web infrastructure and organic/AI search visibility for Watershed's demand gen efforts. Drive pipeline through SEO, LLM optimization, CRO, and hands-on testing while managing vendors and developers.
AI Growth Architect - Fashion
Drives AI-powered growth strategies for fashion brands using Hilbert platform, analyzes customer behavior and revenue drivers, collaborates with sales on account strategies, and provides feedback to improve product reasoning in apparel-specific contexts.
San Francisco, CAGrowth MarketingHybrid
About the role
Core Responsibilities
- Run Hilbert on fashion industry’s growth problems, identify where its reasoning can do better, and translate those findings into structured input for the intelligence layer in coordination with product and tech teams
- Translate hard-won growth experience into the patterns, failure modes, and counter-intuitive signals that shape how the product detects, reasons, and acts on apparel-specific behavior
- Work with Account Executives to turn Hilbert's output into concrete growth strategies for key accounts, from seasonal cohort analysis to category cross-sell intervention to paid channel reallocation around collection launches
- Follow customer's growth with Hilbert, own the consequences with Hilbert
- Represent Hilbert in high-stakes GTM conversations as its most credible voice on what B2C fashion growth actually requires
Who Thrives In This Role (Requirements)
- Owned a growth outcome that was genuinely at risk, not a function that was already working: a post-season retention problem, a new-customer payback period that was not closing, a loyalty cohort that was not coming back
- Understand the fashion purchase cycle at a structural level: the role of new-season drops, markdown strategy and its downstream effects on LTV, the difference between trend-driven and wardrobe-building customers
- Deep, earned fluency in fashion-specific growth mechanics: category and style propensity, seasonal reactivation, subscription and membership models in fashion, influencer and brand-event driven acquisition against LTV
- Think in systems: how acquisition mix shapes retention cohort quality, how discount dependency compounds into a structural LTV problem, how omnichannel data gaps distort what your models see
- Believe in measurability to the maximum: default to testing and learning, to algorithms over human-defined rules, to letting data surface structure rather than imposing it
- Comfortable working alongside AI systems and have strong judgment about where human pattern recognition still wins, especially in a domain where emotional and aesthetic drivers interact with behavioral data
Skills
Data ScienceAIPredictive AnalyticsCRMRetention AnalysisLtv ModelingCohort AnalysisPaid AcquisitionOmnichannel DataGrowth ModelingSeasonal AnalysisCross-Sell Strategies
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