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

KEY RESPONSIBILITIES

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 (YouTube 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.

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