Search Engine Optimization Analyst

QUALIFICATIONS:

• • Bachelor’s degree. (Master's degree preferred).
• • 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).

KEY PERFORMANCE INDICATORS (KPI):

• • 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
• verdict.

KEY RESPONSIBILITIES:

• • Measurement, intelligence and experimentation:
• o 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:
• o 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:
• o 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:
• o 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:
• o 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:
• o 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
• • Compliance Guardrails:
• o 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:
• o 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
• with the wider growth motion.