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Operator MS-0164

Senior AI Sales Development Rep, Certified Senior

Certified
Experience
10 years, B2B SaaS, Agencies, Professional services
Timezone
Central (UTC-6)
Stack
Clay, Claude, Apollo, Instantly, HubSpot, LinkedIn Sales Navigator
Exam evidence
Passed the live work exam using their own AI stack with a verification score of 93 of 100, building a scored account list, an AI enrichment workflow, and a three sequence outbound program in one timed session, with sampled records checked by hand before anything counted as done.
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Representative profile, anonymized. Full profiles are shared at shortlist and confirmed real on request.

Representative operator

Operator MS-0164

This operator archetype is what an outbound function looks like when one senior person owns the entire AI machine. A decade of SDR and sales ops work supplies the judgment: which accounts are worth a sequence, what a real trigger event is, when volume starts costing more in reputation than it earns in meetings. The AI stack supplies the scale: enrichment waterfalls in Clay, research agents that brief each account, drafting workflows that produce personalization a recipient cannot clock as automated.

The verification habit is the differentiator, because outbound is where unchecked model output does real damage. Their workflow samples every AI enriched list by hand before launch and traces every personalization claim to an actual source about the account. Exam graders scored them on rejecting a plausible but unverifiable trigger event rather than sending against it.

The output shape: a functioning multi channel AI outbound program that would traditionally staff two or three roles.

This is a representative, anonymized profile of the kind of operator in the Multistaff network, not a specific named individual. Full profiles are shared at shortlist and confirmed real on request.

Highlights

  • Runs the full AI outbound stack as one system: sourcing, enrichment, personalization, sending infrastructure, and CRM sync
  • Verifies AI enrichment data on samples before any send; bad records are a deliverability and reputation cost they treat as such
  • Personalization at volume without the template smell, drafted by AI agents against real account research and reviewed by a human
  • Keeps pipeline data clean enough that leadership dashboards can be trusted without a data hygiene project

This is an anonymized, representative sample of the certified network. It is not a specific named individual. Real, matching profiles are shared at shortlist and confirmed on request.