Tonic Health

Tonic Health

Growth Lead

San Francisco · Exec

Sponsorship not specified$80k-$120kDetected 318 days ago
RESTA/B TestingMeta AdsInfluencer MarketingCollaborationProblem Solving

About the role

  • Tonic is growing fast and we're just getting started.
  • Join our Growth Team at a pivotal moment, where a few smart moves can 10x our reach over the next year.
  • Most music platforms today are built for consumption.

Responsibilities

  • You're excited to build a product that helps people connect through music and find meaning and belonging in the process.

Nice to have

  • You thrive in fast-paced, 0→1 environments where experimentation beats perfection.
  • You want real ownership - from pitching ideas to launching and scaling results.
  • You're a builder who can blend creativity with analysis to make smart bets and ship fast.
  • You care about working with a tight, humble, high-performing team that values clarity, speed, and impact. 📝

Skills

  • Launch and test 0→1 experiments across influencer, paid, and referral channels
  • Identify and validate new scalable growth loops
  • Analyze early signals to decide what to double down on vs. kill quickly Execution & Collaboration
  • Own timelines and deliverables for multi-channel campaign launches
  • Shape the narrative arc for each campaign - tailoring the story and CTA to different audiences Tracking & Optimization
  • Monitor CAC, conversions, and retention across initiatives
  • Propose and implement A/B tests
  • Turn validated experiments into repeatable playbooks
  • Scale at least one channel to 2-3× current growth while maintaining healthy unit economics
  • Translate early learnings into cross-channel loops that reinforce Tonic's overall growth engine
  • 3+ years in a growth, marketing, or startup role with proven user acquisition results
  • Deeply metrics-driven - comfortable running experiments, analyzing data, and making fast calls

Compensation

  • $80k-$120k

This listing is sourced directly from Tonic Health's careers page and normalized into a canonical job model.