Search Engine Optimization Analyst

Qualifications

• Breadth over single-lever depth credible across earned media, content, technical and video/social and be able to orchestrate them to one outcome.
• 4+ years in SEO, content strategy, digital PR, or growth, with demonstrated movement into AI search /GEO/AEO/LLM visibility.
• Track record owning a function end-to-end (not just executing within one).
• Working credibility across earned media, content, technical SEO/data accessibility, and video/social — able to set a course across all four rather than going deep in just one.
• Demonstrated comfort operating without an established playbook; treats current best practice as a hypothesis to be re-tested, not a fixed methodology.
• High-level fluency in data pipelines, structured data/feeds, and measurement tooling sufficient to brief and evaluate technical partners – ability to build a plus.
• Exceptional leadership and team management skills, with the ability to inspire and motivate diverse teams.
• Proven ability to drive cross-functional outcomes through influence rather than authority — securing buy-in from engineering, brand, and compliance stakeholders.
• Proficiency in data analysis and the tools relevant to AI visibility measurement (share of voice, citation tracking).
• Visibility:
  Share of model across the core prompt basket or topic.
• Authority & Presence:
  Net new citations and mentions across AI-cited sources, plus owned/earned growth on platforms with demonstrated AI-citation weight (video/social folded in here — both are "earn presence in places AI engines draw from").
• Accuracy & Compliance: % of AI-generated product claims matching source-of-truth data, with zero unresolved non-compliant claims outstanding at any time. (Merged since both are integrity metrics — one factual, one regulatory — and a miss on either is the same kind of failure: something false is circulating that shouldn't be.)
• Demand: AI-sourced sessions, conversion rate, and AI-attributed revenue against current baseline.
• Operating Discipline:
  Structured tests run per quarter and median time from hypothesis to

• Measurement, intelligence and experimentation:
  • Stand up and own the system that tells us what's working: share of voice / share of model, citation frequency, sentiment and framing, and AI-sourced traffic and revenue — tracked per surface and over time, against a defined competitive set and prompt basket.
  • Run structured experiments, hold a clear methodology through constant change, and translate it all into a leadership-ready view.
• Earned authority and third-party presence:
  • Influence the sources AI engines draw on: digital PR, authoritative citations, reviews and review surfaces, inclusion and accurate representation in category roundups, and expert/practitioner and creator mentions. Build relationships with the publishers and communities whose content AI systems repeatedly cite.
• Owned content and knowledge assets:
  • Produce content engineered to be cited, not just to rank: question-led, answer structured pages targeting consideration-stage queries; education and FAQ assets; and a well-formed brand entity footprint (knowledge-graph and authoritative-reference presence). Optimize for citation-worthiness rather than mere retrieval.
• Video and social presence:
  • Develop owned and earned presence on the video and social platforms that carry weight in AI visibility (You Tube prominent among them today), coordinated with brand and social — both content the brand creates and mentions it earns.
• Technical and data accessibility:
  • Ensure content and product data are accessible and parse-able to AI crawlers and feeds. Treat technical tactics (structured data, feeds, markup) as hypotheses to test for impact, not articles of faith — invest where they demonstrably help and don't where they do.
• Product and catalog representation accuracy:
  • Ensure AI assistants describes products correctly — ingredients, forms, use cases, differentiators — and maintain a “source of truth” product knowledge layer that feeds owned and earned surfaces (ties into existing product-copy, metafield, and site search mapping work).
  • Apply regulatory-approved copy standards (FTC/FDA structure-function rules) to all output; proactively detect and drive correction of inaccurate or non-compliant claims AI engines attribute to . Partner with — not override — existing claim sign-off. Visibility never outruns compliance.
• Cross Functional enablement and reporting:
  • Make generative search legible across paid, email/retention, e-commerce, supply, and brand; report progress and learnings on a defined cadence; and feed insights both ways.