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.