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The Growth Brain

Turn enterprise urgency into a mid-market motion that is product-led, evidence-led, and clear about net-new customer value.

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Growth
Strategic opening

The Growth Brain

Mid-market GTM, product-led growth, and market intelligence

A chief revenue officer and chief product officer shape, expressed as one hands-on operator.

Turn enterprise urgency into a mid-market motion that is product-led, evidence-led, and clear about net-new customer value.

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A useful first note shows how you think and what you have made.

What you would own

  • The mid-market motion: audience definition, messaging, activation, onboarding, lifecycle, pricing signals, and conversion loops.
  • A market intelligence practice that connects enterprise conversations, competitive movement, buyer language, and product packaging.
  • Distribution agents, research harnesses, CRM loops, and buyer-feedback systems that improve commercial work without making it feel automated.

What we would look for

  • You understand SaaS buyers, product leaders, and the path from strategic interest to actual adoption.
  • You can sell AI-native value without leaning on AI as the value. You care whether a customer would still buy if the implementation detail were invisible.
  • You can move between revenue strategy, product positioning, content, analytics, CRM operations, customer learning, and agent-assisted execution.

Questions you would help answer

  • Which enterprise lessons should shape the mid-market product and which should stay enterprise-specific?
  • What product-led growth motion fits a product that is changing how teams decide what to build?
  • How should market signal become sharper product, content, outreach, onboarding, and packaging each week?
What the work feels like

The product is the operating layer for better product decisions.

The role is not to add AI decoration to old workflows. The role is to preserve product intent from customer evidence to shipped work, while making every loop more inspectable, more reliable, and easier for a human team to trust.

Product intent stays human

AI can accelerate the work, but the reason to build still comes from evidence, taste, tradeoffs, and accountability.

Visible systems beat claims

We look for artifacts: GitHub repos, workbenches, eval notes, customer loops, market systems, prototypes, and operating docs.

The harness matters

Great AI-native work depends on context, evals, guardrails, review loops, and proof that the next cycle gets sharper.

Zentrik agent workflow graph showing product context moving into agent work.
How we think about fit

Range matters, but judgment matters more.

We are more interested in visible systems of work than in claims of AI fluency. Strong candidates can show how they create leverage without confusing output volume for customer value.

GitHub or public work artifacts

Agent harness or eval loop

Product or business judgment

Market or customer learning system

Infrastructure or MLOps system

Community or enablement motion

Personal workbench or knowledge system

Hiring process

Show us the system behind the work.

A strong note could include a GitHub repo, product teardown, prototype, GTM system, research synthesis, eval harness, personal workbench, shipped product, or a short memo about where you think Zentrik should go.

Contact Zentrik
Tell us which role fits your strongest proof.
  1. 1

    Send a proof artifact

    Share a repo, product teardown, prototype, GTM system, research synthesis, eval harness, customer system, or short memo.

  2. 2

    Work through the role shape

    We will talk about the domain, the level of ownership, the systems you have built, and where your judgment is strongest.

  3. 3

    Review a real operating problem

    The useful signal is how you structure ambiguity, design feedback loops, and decide what is worth doing next.

  4. 4

    Define the first scope

    If there is a fit, we will shape the initial domain clearly enough that you can start creating leverage quickly.