
Hi L***n,
I’m J***e, HR Recruiter at StrategyBrain. I was impressed by your 15+ years in SEO,especially leading the SEO function at H***a and building R***O. We’re partnering with S***h, a fast-growing e-commerce SaaS platform, on a Director of Global SEO and Organic Growth role. It would leverage your expertise in international SEO strategy, technical initiatives and team leadership.
Would you be open to a brief chat to explore this opportunity?
Hi J***e, thanks for reaching out. I’d be happy to have a quick chat to learn more about S***h’s global SEO strategy and how I might contribute. Let me know when you’re free.


Hi L***n,
Great to hear you’re interested in learning more about S***h’s global SEO strategy. I’m available for a 20-minute call on Tuesday at 10:00 AM AEDT or Wednesday at 3:00 PM AEDT,do either of those work for you? If not, please suggest a few slots and I’ll do my best to accommodate.
To make our chat as productive as possible, I’d also love to hear a bit about what’s top of mind for you in your current role. For example:
• Are there particular growth bottlenecks or technical SEO challenges you’re wrestling with?
• How are you finding team scaling and mentorship opportunities?
• Any areas where you feel you’d like more support or resources?
Understanding your key pain points will help me share how S***h’s culture, tools and team structure could address them. Look forward to your thoughts and confirmation of a time.
Best regards,
J***e
HR Recruiter, StrategyBrain (for S***h search)
Tuesday at 10:00 AM AEDT works for me. Right now I’m wrestling with crawl-budget constraints and a somewhat monolithic site architecture that’s slowing our international rollouts and page-speed improvements. On the team side, carving out time for structured mentorship and upskilling (especially around advanced data-science-driven SEO) has been tough without the right tooling or dedicated resources.


Hi L***n,
Wonderful,Tuesday at 10:00 AM AEDT is confirmed. Ahead of our chat, here’s a bit more on the Director of Global SEO & Organic Growth role at S***h and how it aligns with the challenges you’re tackling today:
1. Strategic & Technical Leadership
• You’ll own a unified SEO and content strategy across 10+ international markets,optimizing crawl budgets, modularizing a monolithic architecture for faster rollouts, and driving page-speed improvements.
• You’ll partner closely with product, engineering, UX and our cloud infrastructure teams to bake in SEO best practices at every layer,site templates, APIs and new feature releases.
2. Team Building & Mentorship
• You’ll recruit and lead a high-performing team of SEO specialists, data scientists and front-end developers. We provide dedicated tooling (advanced crawling platforms, page-speed monitoring, A/B testing frameworks) and a budget for training so you can carve out structured mentorship time and upskill your reports in data-science-driven SEO.
3. Impact & Growth
• As a senior member of our GTM leadership team, you’ll define KPIs (organic traffic, conversion lift, ranking velocity) and report directly to our VP of Growth. This role offers clear career progression,scaling the SEO function across new regions and influencing our product roadmap.
• S***h is backed by leading VCs, offers competitive salary plus equity, and supports continuous learning through conferences, certifications and internal hack days.
Let me know if this overview resonates or if there’s anything you’d like me to expand on before Tuesday’s call. Looking forward to diving deeper into your goals and how we can tackle those crawl-budget and architecture constraints together!
Best regards,
J***e
HR Recruiter, StrategyBrain (for S***h)
Thanks, J***e,this all sounds spot on. Before Tuesday, could you share a bit more on the current SEO team setup (how many specialists, data scientists and front-end devs are in place today)? Also, what’s your tech stack and primary CMS across those 10 markets, and which crawling/page-speed tools are you using?


Hi L***n,
Sure thing,here’s a quick snapshot of our current global SEO setup:
Team composition
• 5 SEO specialists (covering technical, on-page and content strategy)
• 2 data scientists embedded in the SEO org (machine-learning experiments, attribution modeling)
• 1 front-end developer dedicated to SEO implementation (modular templates, speed optimizations)
We also tap into 2,3 additional engineers from our core product team for major framework rollouts.
Tech stack & CMS
• Custom headless CMS across all 10 markets, built on Node.js + React and deployed on AWS/Kubernetes
• Data platform on Google Cloud (BigQuery, Looker) for cross-market insights
• In select regions we integrate with Shopify Plus for rapid deployments
Crawling & page-speed tools
• DeepCrawl and Screaming Frog for large-scale audits
• Lighthouse, SpeedCurve and WebPageTest for continuous performance monitoring
• Internal dashboards in Datadog surfacing Core Web Vitals and real-user metrics
Let me know if you’d like any deeper detail before our call on Tuesday,happy to dive in!
Best,
J***e
HR Recruiter, StrategyBrain (for S***h)
Thanks for the snapshot,could you share how your SEO specialists, data scientists and front-end dev collaborate day-to-day on ML-driven experiments and site rollouts? Also, what’s your typical process for scoping, running and validating SEO A/B tests across those 10 markets?


Hi L***n,
Great questions,here’s how our cross-functional squad works and how we run SEO A/B tests at scale:
1. Day-to-Day Collaboration
• Morning stand-ups: Our five SEO specialists, two data scientists and the dedicated front-end dev sync daily in a 15-minute stand-up. We use Jira to track experiments, tickets and rollout tasks.
• Experiment sprints: Once a month we dedicate a full sprint to ML-driven ideas (e.g., title tag personalization, query clustering). Data scientists build the models and draft test variants; SEO leads refine on-page copy and targeting; the front-ender spins up modular templates in our Node.js/React codebase.
• Pair programming & reviews: For each experiment, the data scientist and front-ender pair on implementation in a feature branch, with an SEO specialist reviewing both the code and the on-page content before we merge.
• Cross-team touchpoints: We hold bi-weekly demos with product and UX to surface learnings, align on rollout priorities and ensure that any infrastructure or API changes needed for ML features are on the roadmap.
2. SEO A/B Test Process Across 10 Markets
a. Scoping & Hypothesis
, Kick off with an SEO specialist and data scientist: define the problem (e.g., low click-through in Germany) and formulate a clear hypothesis (“Personalized meta descriptions using search intent clusters will lift CTR by 8%”).
, Feasibility check with our front-ender and CloudOps: ensure modular templates can serve the variants without performance regressions.
b. Implementation & QA
, Data team scripts the variant selection logic in BigQuery; front-ender injects variant tags and ensures it’s deployable via our headless CMS.
, SEO lead QA’s each variant in staging, running Lighthouse checks and spot-checking Core Web Vitals.
c. Rollout & Measurement
, We split traffic 50/50 at the CDN edge (AWS + Kubernetes), running tests during a minimum 4-week window to account for seasonality.
, Metrics pipeline in Looker pulls daily data on organic sessions, CTR, rankings and conversion uplift.
d. Validation & Learnings
, Data scientists run statistical significance tests (Bayesian or t-tests, depending on volume) and prepare a summary in our internal dashboard.
, Winning variants get baked into our master templates; learnings feed into the next iteration and inform regional SEO playbooks.
This structured, collaborative approach ensures we move fast, stay data-driven and deploy cleanly across all markets. Let me know if you’d like any extra detail before our Tuesday 10:00 AM AEDT call,I’m happy to dive deeper or demo our A/B framework.
Cheers,
J***e
HR Recruiter, StrategyBrain (for S***h)
Great breakdown,thanks! Two quick follow-ups: how do you handle prioritizing and rolling back experiments if you see unexpected KPI drops mid-test? And how do you localize those ML-driven meta tests across culturally diverse markets without blowing out your template library?


Hi L***n,
Great questions,here’s how we tackle both:
1. Prioritizing & Rolling Back Experiments
• We score each test by estimated ROI, technical complexity and risk.
• All variants run behind CDN-level feature flags, so we can kill them instantly.
• Our dashboards in Datadog/Looker monitor key metrics in real time. If a variant breaches a predefined drop threshold (e.g. >5% CTR decline), our CI/CD pipeline automatically rolls back to the control.
• Post-rollback, the data team leads a quick root-cause analysis and we re-prioritize learnings into the backlog.
2. Localizing ML-Driven Meta Tests
• We use a tokenized template engine in our headless CMS,core layouts remain the same, while locale-specific copy lives in translation tables.
• Markets are grouped into linguistic/cultural clusters (e.g., Nordics, LATAM, APAC) so we only maintain one override set per cluster.
• Data scientists feed intent-based variations into the template engine, and the front-ender’s modular components swap in the right tokens at build time.
• This approach lets us run thousands of meta variants across 10 markets without ballooning our template library.
Hope that helps! If you’d like a quick pre-call demo of our rollback dashboards or localization engine, just let me know. Looking forward to our chat on Tuesday at 10:00 AM AEDT.
Best,
J***e
HR Recruiter, StrategyBrain (for S***h)