Mutinyhq

Mutinyhq

AI Strategist

In person in New York City

Sponsorship not specifiedDetected 84 days ago
SnowflakeCustomer Success

About the role

  • Most CS roles are graded on how few problems the customer has.
  • It's how we turn early traction into the customer base that defines a category.
  • This role is in person in New York City, five days a week.

Responsibilities

  • CS isn't a support function here.
  • You'll build the playbooks as you go and shape what customer success looks like at an AI-native company from the ground up.
  • EBRs that move things. Run business reviews that tell a real story and drive action. Follow-ups that are crisp and lead somewhere.
  • Teach customers how to use our agent-first platform to create assets that actually move the needle for their sales and marketing teams.
  • Run business reviews that tell a real story and drive action.
  • Follow-ups that are crisp and lead somewhere.
  • Build the case six months before it comes up.
  • When you find a better onboarding flow, a sharper EBR, or a new play that's working, you document it.
  • You create "aha" moments by tailoring advice to what each customer actually needs.
  • You're building the case for why a customer should stay and grow, not scrambling when it comes up.

Benefits

  • Adoption and customer health are the leading indicators.
  • You figure things out, document what works, and leave it easier for the next person.
  • Adoption and health are the leading indicators you watch.

Company info

  • The AI motion. Teach customers how to use our agent-first platform to create assets that actually move the needle for their sales and marketing teams. Their team uses Mutiny because you made it obvious how.
  • As a growth strategist at Mutiny, you'll own a book of managed accounts and become the reason teams at customers like Rippling, Uber, and Snowflake get more out of AI than their peers.
  • Customers feel like you're genuinely in their corner.
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This listing is sourced directly from Mutinyhq's careers page and normalized into a canonical job model.