AI Growth Architect - Fashion
Hilbert's AIData & company
San Francisco, United StatesSenior
Andreessen Horowitz
SV Angel
Asylum Ventures
ScaleX Ventures
Data & AI
About the role
TL;DR
Run Hilbert on fashion industry growth problems and improve its intelligence layer.
- •Your responsibilities look like those of a founding operator embedded simultaneously inside a high-stakes product and commercial organization.
- •You run Hilbert on real problems: you are expected to be its most demanding internal user, finding where the product's reasoning breaks down, challenging its outputs, and feeding that back into the intelligence layer as structured improvement input.
- •Key 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 Requirements You have 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 You 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 You have 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 You 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 You believe in measurability to the maximum: you default to testing and learning, to algorithms over human-defined rules, to letting data surface structure rather than imposing it You are 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 Specific matters more than impressive: the best candidates can describe a fashion growth failure with the same precision as a win And most importantly: you believe there are fundamental problems with how the fashion industry has thought about customer growth, and you are ready to rebuild it
Required skills
Growth Strategy
Domain expertise
ecommerce